Learning Bayesian Statistics

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I’ll be honest — this episode is long overdue. Not only because Ben Vincent is a friend, fellow PyMC Labs developer, and outstanding Bayesian modeler. But because he works on so many fascinating topics — so I’m all the happier to finally have him on the show!

In this episode, we’re gonna focus on causal inference, how it naturally extends Bayesian modeling, and how you can use the CausalPy open-source package to supercharge your Bayesian causal inference. We’ll also touch on marketing models and the pymc-marketing package, because, well, Ben does a lot of stuff 😉

Ben got his PhD in neuroscience at Sussex University, in the UK. After a postdoc at the University of Bristol, working on robots and active vision, as well as 15 years as a lecturer at the Scottish University of Dundee, he switched to the private sector, working with us full time at PyMC Labs — and that is a treat!

When he’s not working, Ben loves running 5k’s, cycling in the forest, lifting weights, and… learning about modern monetary theory.

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Trey Causey, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony and Joshua Meehl.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag 😉

Links from the show:

Abstract

written by Christoph Bamberg

In this podcast episode, our guest, Ben Vincent, a fellow member of PyMC Labs with a PhD in Neuroscience and extensive experience in teaching and data analysis of course, introduces us to CausalPy and PyMC Marketing.

During his academic career, Ben got introduced to Bayesian statistics but, like most academics, did not come across causal inference. 

We discuss the importance of a systematic causal approach for important questions like health care interventions or marketing investments. 

Although causality is somewhat orthogonal to the choice of statistical approach, Bayesian statistics is a good basis for causal analyses, for example in the for of Directed Acyclical Graphs. 

To make causal inference more accessible, Ben developed a Python package called CausalPy, which allows you perform common causal inferences, e.g. working with natural experiments.

Ben was also involved in the development of PyMC Marketing, a package that conveniently bundles important analysis capacities for Marketing. The package focuses on Media Mix Modelling and customer lifetime analysis. 

We also talked about his extensive experience teaching statistics at university and current teaching of Bayesian methods in industry. His advice to students is to really engage with your learning material, coding through examples, making the learning more pleasurable and practical. 

Transcript

Please note that this is an automated transcript that may contain errors. Feel free to reach out if you’re willing to correct them.

Transcript
[Alex]:

OK, so we are live, Ben Vincent. Welcome to Learning Beijing Statistics.

[Ben]:

Hi Alex, it's very good to be here.

[Alex]:

yeah I'm really happy to have you on the show because we've been working together

[Alex]:

for quite a while now and I wanted to have you on the show for a while but wanted

[Alex]:

to find the right angle because you do so many things that I was like I don't know

[Alex]:

which one we should focus on so fortunately I made my choice and today we're mainly gonna

[Alex]:

talk about causal inference You do a lot of things, so we'll see where the discussion

[Alex]:

takes us. Also, I shared that recording link in the slack channel of the patrons of

[Alex]:

the show, so maybe some of them will show up at some point in the audience and we'll

[Alex]:

have some live questions. Maybe it will just be the two of us. We'll see what life

[Alex]:

has for us. Yeah, basically that's it from my end. A quick note that I am, as you

[Alex]:

may hear, a bit sick. So I'm going to try to cough as little as possible, but I cannot

[Alex]:

make... that's the only promise I can make. Okay, so let's dive in. And as usual,

[Alex]:

let's start with your origin story, Ben. So how did you come to the world of...

[Alex]:

to the fabulous world of statistics and probabilistic modeling, and how sinuous

[Alex]:

of a path was it.

[Ben]:

Yeah, so it certainly wasn't direct. So I think most people I know have some kind

[Ben]:

of formal education in stats training to some capacity, whereas I didn't really have

[Ben]:

any. So for many years I was teaching, I was faculty in an experimental psychology

[Ben]:

department. And of course there... we start the undergraduates off quite early with

[Ben]:

stats. Of course, it's all frequentist, but I didn't have any of that. My undergrad

[Ben]:

and PhD was basically neuroscience.

[Alex]:

Mm-hmm.

[Ben]:

And while clearly it's a quantitative topic, you didn't really get the same immersion

[Ben]:

in statistical training. that you would in experimental psychology, for example. So

[Ben]:

I was definitely a latecomer. That said, I think my PhD in postdoc prepared me well

[Ben]:

on the kind of quantitative way of thinking, particularly programming and things like that.

[Ben]:

So my background with coding started at a very young age. And

[Alex]:

Oh

[Ben]:

I

[Alex]:

yeah.

[Ben]:

think that... Yeah, so it started off probably when I was about seven.

[Alex]:

Oh damn.

[Ben]:

And yeah, so I learned BBC basic very early on and I think that was

[Alex]:

BBC

[Ben]:

that was

[Alex]:

basic

[Ben]:

a

[Alex]:

is that related to the BBC, the British broadcast channel in any way or not?

[Ben]:

It is, yeah, it is. So I'm not 100% sure exactly how that came about, but in the kind

[Ben]:

of relatively early days of computing, I think that there was this kind of clear prediction

[Ben]:

that people made that computing was gonna be a big thing. And so the BBC as kind of in

[Ben]:

its kind of educational capacity started to kind of get involved in this. and they maybe

[Ben]:

supported the development of a language for, I think it was like BBC micro systems

[Ben]:

or Acorn micro systems. I can't quite remember. I was only seven.

[Ben]:

So yeah, that was a very kind of early introduction into coding. And I think people...

[Ben]:

who code obviously kind of appreciate that it makes you think slightly differently.

[Alex]:

Mm-hmm. Yeah.

[Ben]:

So it gives you a good foundation, I think. Anyway, so my PhD kind of involved modeling

[Ben]:

neural networks. And so the basic idea was to answer a question really about the human

[Ben]:

brain, about physiology. So the question is, how does the brain actually wire itself

[Ben]:

up? Are there any underlying principles that we can use in order to kind of understand

[Ben]:

this very complicated organ? And my particular PhD basically was looking at the question of

[Ben]:

the role of energy efficiency in neural networks. And

[Alex]:

Okay.

[Ben]:

we basically use the visual system. because as like a model system, because the neural

[Ben]:

wiring of the visual system is really well known.

[Alex]:

Mm-hmm.

[Ben]:

So what that allowed us to do was to make these little neural network simulations

[Ben]:

and put in various different costs, like for example, similar to what we now know as

[Ben]:

weight decay in machine learning. or you could even think of it as like Laplacian

[Ben]:

priors from the Bayesian perspective. But from a physiology perspective, you can

[Ben]:

basically think of these things as energetic costs. So for every connection between one

[Ben]:

neuron and another, you can imagine that that takes energy in order to transmit information

[Ben]:

through the synapses. And you also have costs. simply for the number of neurons that

[Ben]:

you have. So we were trying to understand why we have this quite peculiar arrangement

[Ben]:

of the visual system. So anyway, we were doing coding, a very early neural network stuff,

[Ben]:

compared to the things that people do now, it was trivial. So this was back in the

[Ben]:

era of shallow neural networks and also... wasn't any Bayesian stuff in sight at this

[Ben]:

point. So later on I kind of started to get more exposed to, well in fact during

[Ben]:

my PhD I was kind of exposed to the Bayesian way of thinking through my

[Alex]:

Mm-hmm.

[Ben]:

supervisor, that's Dr. Roland Baddeley, and the way how he presented it was sufficiently

[Ben]:

like simple. in terms of the conceptual basis of Bayesian inference is seductively

[Ben]:

simple. And so you kind of think that you get it straight away.

[Alex]:

No.

[Ben]:

But then when you try to actually think about it in concrete terms and maybe implement

[Ben]:

something without a whole bunch of additional stuff, it actually starts to get quite tricky.

[Ben]:

So I spent a long... period where I was kind of convinced that the Bayesian approach

[Ben]:

was definitely interesting and it sounded sufficiently contrarian for me to be interested.

[Ben]:

But it wasn't maybe until the postdoc phase and early in my lectureship that I felt

[Ben]:

like I really

[Ben]:

had like a decent understanding to the extent that I could code these things up myself.

[Alex]:

Yeah that's super cool, I didn't know you started so early actually in your BBC time.

[Alex]:

And yeah I do agree that it's really cool to start so early, not really for their

[Alex]:

language barrier, but I would say as you were saying for the kind of skills. that

[Alex]:

it gives you in the sense that it teaches you how to basically fail all the time and

[Alex]:

with be okay with failure and iterative improvement and in a way not only looking for

[Alex]:

success but also being comfortable. failing in a way which is something that at

[Alex]:

least in my education in France lacked a lot. I don't know

[Ben]:

Hehehe

[Alex]:

about now but it's like really trying to make you say the things that you have to

[Alex]:

say to have a good grade instead of making you think through a solution. So really

[Alex]:

rewarding the result much more than the path to get to the result.

[Ben]:

Yeah, completely. I think the way how... The particular thinking skills it gives you,

[Ben]:

I think are fantastic. And I didn't think about it back then, but the way how I might

[Ben]:

think about it now is... I was doing, you know, silly stuff. Like, I was simulating

[Ben]:

what happens if you have, like, little bacteria, which are just represented by pixels

[Ben]:

on your screen. and they kind of move around and there's food and every time they

[Ben]:

get enough food, then they replicate but if they don't get enough food, then they

[Ben]:

die. So I kind of, you're basically creating your own miniature simplified world where you

[Ben]:

have clear, well-defined rules. And I might call that now like a data generating

[Ben]:

process. because you can tweak, you know, what's the lifespan, how much food do they

[Ben]:

need, and different kinds of outcomes happen. So I really wish at some point, even

[Ben]:

at that point, I think my brain would have been amenable to Bayesian thinking because

[Ben]:

there you're basically just doing the opposite of that.

[Alex]:

Hmm. Yeah. Yeah. Yeah. Talking about Bayesian thinking. Do you remember when you

[Alex]:

were first introduced to Bayesian methods and why they stuck with you?

[Ben]:

Yeah, so I think broadly speaking, the core kind of introduction to the Bayesian ideas

[Ben]:

was through my supervisor, Roland Baddeley. But because the Bayesian ideas didn't directly

[Ben]:

relate to the PhD work that I was doing, the actual concrete implementation of it was

[Ben]:

maybe left a little bit opaque at that point. So I think really the exact point is

[Ben]:

kind of hard to define, but during my postdoc and early faculty time, I started to

[Ben]:

kind of shift just from making models which you kind of qualitatively compare to

[Ben]:

something that you might know and see if it's a good fit. To now try to... quantitatively

[Ben]:

compare these models that you create to what we know about how people behave. So

[Ben]:

in particular, coming at it from like a cognitive, experimental, psychology perspective,

[Ben]:

we were trying to model visual attention. So

[Alex]:

Mm-hmm.

[Ben]:

both overt visual attention, which is basically, how do I move my eyes around? in

[Ben]:

order to gather information that I might need in order to solve problems and complete

[Ben]:

tasks. But then you also have this thing called covert visual attention, which is maybe

[Ben]:

what most people kind of think of when when that phrase is mentioned is basically

[Ben]:

if you have a whole bunch of information coming in, some of that stuff is going to be

[Ben]:

more relevant to you than other stuff. And so if you're... If you can narrow your

[Ben]:

attention on the kind of appropriate information, then you're essentially increasing

[Ben]:

your signal to noise ratio.

[Alex]:

Mm-hmm.

[Ben]:

You're gathering in the information that is informative to help make a decision and

[Ben]:

kind of eliminating some of the information that is not relevant. And so we tried to set

[Ben]:

up experiments both with eye movements and covert attention. where we basically put

[Ben]:

real people in psychology experiments. We made them look at computer screens and we

[Ben]:

showed them different kind of simplified visual situations and we gave them certain

[Ben]:

tasks. And kind of our approach as modelers was to basically think up of possible ways,

[Ben]:

like what's going on inside this black box brain. that is actually leading them to

[Ben]:

behave in a certain way, solve a problem in a certain way. Um, so, so the, the core

[Ben]:

of that, the, the way, the kind of Bayesian way of thinking that Roland, um, introduced

[Ben]:

to me was basically translated into that. So we were essentially asking, um, how would

[Ben]:

we expect people to behave or how would we expect them to solve a problem if they were

[Ben]:

Bayesian. So stepping back a little bit, I kind of see it almost as jumping into the

[Ben]:

kind of slightly more complex side of Bayes before the kind of easier side. So the

[Ben]:

easier side, I guess, or the main way how Bayes is kind of brought up to people is

[Ben]:

through like describing your data. So what we might call like regular. statistics.

[Ben]:

But Bayesian modelling is a little bit slightly different in that you're not just

[Ben]:

trying to describe the data as such, but you're more actually trying to really describe

[Ben]:

the data generating process and that in this case involved like human cognition.

[Alex]:

Yeah, I see. Yeah, super interesting. And so that's how basically you started doing

[Alex]:

bass and now you are basically doing that full time. Right? So maybe define the

[Alex]:

work that you're doing nowadays for listeners and also the topics that you are

[Alex]:

particularly interested in.

[Ben]:

Yeah, sure. So the main stuff that I'm doing at the moment is basically working as

[Ben]:

a consultant data scientist. And most of that stuff is through PMC labs. And that's

[Ben]:

largely how we know each other. I do a little bit of random consulting on the side,

[Ben]:

but the majority of it is through PMC labs. So we do a bunch of different things.

[Ben]:

part of it is educational. So I think after all my years of teaching in academia,

[Ben]:

that maybe set me up to enjoy teaching Bayesian methods to people in industry. But

[Ben]:

we also just develop novel models and solve hard problems for people who have kind

[Ben]:

of... challenging data science questions in order to solve business problems in industry.

[Ben]:

So that's the majority of what I'm doing.

[Alex]:

that sounds like fun

[Ben]:

It is.

[Alex]:

yeah and actually you do a lot of of causal inference we'll get back to the to the

[Alex]:

teaching aspects if we have time at the end of the show because that's always also

[Alex]:

something that i'm very interested in but yeah like to start diving in a bit about

[Alex]:

your main focus these days let's talk about causal inference. So can you define causal

[Alex]:

inference for us and tell us how it differs, if it differs from Bayesian stats?

[Ben]:

Yeah, so just to kind of put this in context, I thought that over the years of exposure

[Ben]:

and kind of experience to Bayesian inference that I was a reasonably clued up scientist

[Ben]:

and a now data scientist. But then I kind of became aware through a really bizarre

[Ben]:

kind of situation. that I was actually completely clueless about causality. And I

[Ben]:

think that this is actually like a, something that bubbles away in the brains of many academics

[Ben]:

because in the same way that I didn't have formal training in statistics, I think

[Ben]:

that unless you're kind of lucky, maybe you're in an area of epidemiology, for example,

[Ben]:

I think it's extremely rare to get formal training in... causal inference. And so a lot

[Ben]:

of the ideas that we, we gather over time about causality focus mostly on kind of

[Ben]:

heuristics and things like this and not very much on kind of formal ideas about what

[Ben]:

causality is.

[Alex]:

Mm-hmm.

[Ben]:

So yeah, do you want me to go on and describe? like what causality is or expand

[Ben]:

a bit on how I got interested in causality.

[Alex]:

Yeah, maybe define a bit the causality part, basically to set the ground up for people

[Alex]:

so that they understand the different specification stats. And then we'll dig into

[Alex]:

causal pi to actually do causal inference in the Bayesian framework.

[Ben]:

Sure. So, I mean, one way how you can slice things up is to basically think of statistical

[Ben]:

relationships as being kind of one particular thing. And then you could further subdivide

[Ben]:

that into, you know, are you gonna take a frequentist approach or a Bayesian approach?

[Ben]:

And the other kind of angle would be looking at like, at... causal relationships.

[Ben]:

And you could almost think of that as orthogonal to what particular statistical approach

[Ben]:

you use, whether it's frequentist or Bayesian. But really the way how I would kind

[Ben]:

of describe a causal relationship, I think this is gonna be fairly close to the

[Ben]:

kosher definition, is that a relationship can be said to be causal if you go in and intervene

[Ben]:

um on the cause and that then changes uh the effect and that opens up a whole bunch

[Ben]:

of questions like how do you know uh whether this thing really causes something

[Ben]:

else because you can't simultaneously um kind of fall off a bike and not fall off

[Ben]:

a bike so uh yeah i think the the key

[Ben]:

If you intervene and that changes an outcome, then you can describe that relationship

[Ben]:

as causal. Anything else is basically statistical. There's no directionality there.

[Alex]:

I see. And so my kind of question would be, well, then why aren't we doing that all

[Alex]:

the time? And are there circumstances in which causal inference is most helpful?

[Ben]:

Yeah, so in terms of why aren't we doing that all the time is an exceedingly good question,

[Ben]:

because the way, if you were to kind of list out the order of topics in a book,

[Ben]:

in terms of how people might come across it, or the level of perceived difficulty, then

[Ben]:

it might be something like linear modeling type of stuff. like t-tests, ANOVAs, and then

[Ben]:

you might learn kind of hierarchical stuff, and then you might learn, oh, all of

[Ben]:

this stuff that I learned can be approached from the Bayesian perspective, and life actually

[Ben]:

gets much simpler if you're a Bayesian, and so you might ask kind of, why didn't they

[Ben]:

just teach me the Bayesian stuff to begin with?

[Alex]:

Mm-hmm.

[Ben]:

But then, you know, you're looking toward the back of the book, the more complex chapters.

[Ben]:

on causality and you think, wow, this is like advanced stuff. But again, uh, I'm

[Ben]:

kind of asking myself, why, why isn't this stuff taught as, um, why isn't there a

[Ben]:

module, for example, that all undergraduates take in year one on, on causal inference, because,

[Ben]:

um, this stuff can actually be taught without getting that quantitative, to be honest,

[Ben]:

largely. Like the majority of it can just be taught with a set of ideas and words

[Ben]:

and just concepts without having to get into maths or coding.

[Alex]:

Mm-hmm.

[Ben]:

That certainly does like add to your level of understanding. But yeah, I don't really

[Ben]:

know a good answer, but it's probably the same as why aren't we all learning Bayesian

[Ben]:

stuff first rather than frequentist stuff?

[Alex]:

yeah and do you think it could be due to the fact that because I mean definitely

[Alex]:

one of the reasons that not everybody is using patient status well um sometimes it's

[Alex]:

not the right tool but also like historically it's been a bit harder to do now it's way easier

[Alex]:

uh and so like with path dependency things are take always a bit of time to to

[Alex]:

compensate, right?

[Ben]:

Yeah.

[Alex]:

So is there something related with causal inference? And it's actually a question I wanted

[Alex]:

to ask you, which are mainly, what are some of the challenges or limitations that

[Alex]:

practitioners face when doing causal inference currently? Because that could be

[Alex]:

also a factor that impede the usefulness and the utility of causal inference tools.

[Ben]:

Um, yeah, so I think. So just stepping back slightly, I think that you're right, there

[Ben]:

is a historical kind of effect that probably explains why we don't all learn causal inference

[Ben]:

in primary school.

[Alex]:

Mm-hmm.

[Ben]:

I think going forward that doesn't necessarily have to be the case because now just in the

[Ben]:

same way that we have really great kind of educational material for Bayesian inference

[Ben]:

and we have really cool kind of relatively easy to use probabilistic programming languages

[Ben]:

We That stuff can be bootstrapped. I guess to teach you about Causal inference so

[Ben]:

I found as I was kind of learning and I'm still very much learning about causal inference

[Ben]:

I found that a lot of those concepts were easier having a background in Bayesian

[Ben]:

inference, because you already know about the concepts of directed acyclic graphs, for

[Ben]:

example, and you already know about observing variables. And so there are only like a handful

[Ben]:

of key concepts that you need to learn on top of that before you can start really

[Ben]:

diving in and start simulating real things to... to get a sense that you really understand

[Ben]:

what's going on.

[Alex]:

Yeah, I mean to me the Bayesian framework is really close already to the causal inference

[Alex]:

framework because that generative modeling perspective is so ingrained in the Bayesian

[Alex]:

framework that once you're here already, well that you already have a foot in the door

[Alex]:

in a way.

[Ben]:

Yeah, I mean the way of kind of describing models in terms of these directed acyclic graphs

[Ben]:

where you have these kind of pointy arrows and in Bayesian inference that kind of implies

[Ben]:

causality but it doesn't kind of necessitate it as such. But yeah, once you know some of

[Ben]:

those concepts then it's not a whole bunch more you need to learn on top of that

[Ben]:

before you can really feel like you're getting somewhere. Um, but I think I missed

[Ben]:

out one of your, uh, questions earlier of, uh, like under what circumstances can cause

[Ben]:

winfrents be most helpful. Um, so

[Alex]:

Right.

[Ben]:

I, I missed that before.

[Alex]:

I mean, I think I interrupted you.

[Ben]:

Ah,

[Alex]:

So it's all on me.

[Ben]:

so

[Alex]:

But yeah, go ahead.

[Ben]:

yeah,

[Alex]:

Good point.

[Ben]:

so in terms of my own question then, I mean, I think that there are a whole bunch

[Ben]:

of situations where it can be incredibly useful. So the first one is where there are

[Ben]:

a bunch of things called statistical paradoxes. And we all know that kind of paradoxes

[Ben]:

typically are just not really paradoxes but just expose not really the correct understanding

[Ben]:

of how things work. So you can come across certain results, certain kind of empirical

[Ben]:

results and relationships between variables that really make you scratch your head and

[Ben]:

confuse you if you're coming from purely a statistical point of view.

[Alex]:

Mm-hmm.

[Ben]:

But once you start adding on a layer of causal thinking on top of that, all of a

[Ben]:

sudden, there are kind of very intuitive and appealing explanations for these things

[Ben]:

that previously were quite confusing. So I guess that that's like an intellectual

[Ben]:

and practical thing. There's just moving from a situation where you don't really understand

[Ben]:

the data that you're looking at. to getting a much better understanding. I think the other

[Ben]:

situation that I would say is really key when causal inference is most helpful is

[Ben]:

when you are doing kind of high stakes interventions. So...

[Ben]:

If you take purely a statistical point of view, you could be trying to do something

[Ben]:

like predict house prices based on a whole bunch of different predictor variables.

[Ben]:

And let's say your data set comes entirely from a low inflation era or a low interest

[Ben]:

rate era. And you, um, maybe you do have the interest rates in your model, but the,

[Ben]:

the way in which you've structured your, your model and your causal understanding is,

[Ben]:

um, purely from like a statistical perspective and not necessarily from a causal perspective.

[Ben]:

And so if you, uh, go in and change things, like you start, um, marketing in a different

[Ben]:

way, for example. or you move into a different regime where interest rates are much

[Ben]:

higher, you could come up with like embarrassingly wrong predictions when you take purely a statistical

[Ben]:

approach. Taking a causal approach definitely does not guarantee that you're always gonna

[Ben]:

make fantastic predictions. But I think it would in general. make you a little bit

[Ben]:

less wrong than if you took purely a statistical approach.

[Alex]:

Yeah, yeah, yeah, in the kind of like in the long run that should be the best bet.

[Ben]:

Yeah, and when the stakes are very high, then that becomes increasingly important. So

[Ben]:

in medical situations, for example, if you are going to intervene in the form of like

[Ben]:

a surgery or a lifestyle intervention,

[Ben]:

you don't want to be doing that on some kind of downstream effect that like treating...

[Ben]:

a symptom for example, rather than an underlying cause.

[Alex]:

yeah for sure and I mean actually I think we're pretty well set up now to talk

[Alex]:

a bit more in detail with causal pie which is one of the open source packages that

[Alex]:

you've single-handedly developed for all of us in the world so yeah can you give

[Alex]:

us an overview of causal pie and tell us how it tries to solve these challenges

[Ben]:

Yeah, so, yeah, I mean, just to say, I kind of led the coding efforts, but I did

[Ben]:

definitely have some input and help from people. So yeah, so CausalPy is a Python-based

[Ben]:

package for doing causal inference. Now, just like how Bayes is a particularly large

[Ben]:

area, large field of study, so is causal inference. And so we absolutely do not try

[Ben]:

to create one overall, overarching package that deals with all causal problems. So we

[Ben]:

completely ignore a whole bunch of areas in the causal space. Like one thing that we

[Ben]:

completely ignore is what's called causal discovery. So this is where you may be given

[Ben]:

a bunch of data. and your task might be to discover what the causal relationships

[Ben]:

are between the variables kind of automatically. And that's something that is hard and there

[Ben]:

are many packages already developed that try to do that with varying levels of success.

[Ben]:

Rather, so CausalPy focuses on a small domain called quasi-experiments. So Quasi experiments

[Ben]:

are also known as natural experiments, and they differ in one key way from real experiments,

[Ben]:

namely that we do not have randomization in how we assign people or countries or companies,

[Ben]:

whatever the unit of measurement is, we do not randomly assign them to different

[Ben]:

groups. And so... there are many different situations where that can arise. So an example

[Ben]:

might be Brexit. So if you think of Brexit as an experimental intervention and think of,

[Ben]:

well, what are the downstream effects of that intervention, in an ideal world, what

[Ben]:

you might do is conduct a randomized control trial and randomly assign Brexit type

[Ben]:

events to a bunch of different countries and see what happens and the idea there is

[Ben]:

that you're taking away the influence of confounding variables so in this Brexit

[Ben]:

example you could think of each country has different compositions of Social and political

[Ben]:

and economic thoughts for example and different economic conditions that the people

[Ben]:

are exposed to. Now, the problem, if you try to kind of ask the question of what's

[Ben]:

the causal impact of Brexit, is that...

[Ben]:

If you look at the consequences, you don't know very convincingly whether those

[Ben]:

consequences are due to the actual treatment, the Brexit, or the underlying confounding variables

[Ben]:

of the particular socioeconomic situation at the time. So there are all kinds of situations

[Ben]:

like this where you just can't run a randomized control trial. And so you're left

[Ben]:

with... Quasar experiments, natural experiments, also known as kind of observational data sets.

[Ben]:

And all of these different situations can be subtly different, have different types

[Ben]:

of characteristics. And so there are a family of analysis methods that have evolved

[Ben]:

over time in order to basically help you analyze data in different types of situations.

[Ben]:

CausalPy tries to bring together into one package a whole set of these things, but

[Ben]:

present a kind of a unified API and try to kind of strip down the ideas to kind of

[Ben]:

core basics so that it becomes much more approachable for people who want to run these

[Ben]:

kinds of analyses.

[Alex]:

Hmm. Yeah. That's super interesting. And, um, I mean, is there any, uh, real-world

[Alex]:

applications of CuzzlePie that you'd like to highlight today.

[Ben]:

Yeah, yeah. So, aside from the kind of Brexit application,

[Alex]:

Mm-hmm.

[Ben]:

and that has actually been used, another kind of really cool real-world one, which we're

[Ben]:

kind of seeing as particularly relevant in the kind of marketing domain, is this idea

[Ben]:

of geo-experiments. So, the idea here is that

[Ben]:

If you launch, let's say a marketing campaign or you do store renovations, you can't

[Ben]:

randomly assign which individual people are then exposed to that because presumably

[Ben]:

it's gonna be, if you do store renovations in an entire country, then everybody who goes

[Ben]:

to that store is gonna be exposed to it. Similarly, if you... launch a new advertising

[Ben]:

campaign, you're not randomizing which individuals are exposed to that. The idea of

[Ben]:

geo experiments are basically that you have different geographical areas, some of

[Ben]:

which are exposed to some treatment or intervention. And I mentioned the examples

[Ben]:

could be a marketing campaign or like store renovation, that kind of thing. Um, so

[Ben]:

the question you would have is, you know, did the intervention that maybe cost me money

[Ben]:

to do, uh, did that actually have a causal influence on an outcome that I'm interested

[Ben]:

in like sales, for example, or, uh, would that, would those changes have happened, uh,

[Ben]:

regardless? Um, and so that is tricky because. you don't necessarily have a random

[Ben]:

assignment of which geographical region was actually exposed to the treatment. And

[Ben]:

you are likely to get systematic differences between each of the geographical regions. So

[Ben]:

it's quite likely that you have these kind of confounding variables that might influence

[Ben]:

the outcome, you know, other than the treatment that you're interested in. So for

[Ben]:

that. CausalPy can help because we have a synthetic control analysis method in there,

[Ben]:

which you can basically use. And this is one of the examples that we've actually

[Ben]:

put into the package.

[Alex]:

Mm-hmm.

[Ben]:

So

[Alex]:

So

[Ben]:

yeah,

[Alex]:

yeah.

[Ben]:

many, many like real world applications of that.

[Alex]:

yeah yeah and will definitely put these links into the show notes they are on the documentation

[Alex]:

for causal price for sure folks feel free to check this out and I maybe something

[Alex]:

I'm I wanna precise a bit is that idea of quasi experiments. So maybe can you define

[Alex]:

that a bit more for listeners because I think it's the first time we've mentioned

[Alex]:

that on the show. So yeah, like what is a quasi experiment and when would that be

[Alex]:

useful?

[Ben]:

Yeah, so quasi-experiments were also known as natural experiments. So I think one

[Ben]:

of the key characteristics of, let's say, real experiments or randomized control

[Ben]:

trials would be the idea of a control group and a treatment group. And so you have

[Ben]:

your intervention, whatever it may be, like... vitamin D supplementation as compared

[Ben]:

to not having vitamin D supplementation.

[Alex]:

Mm-hmm.

[Ben]:

So if you can, then the best thing to do would be to randomise which people get vitamin

[Ben]:

D supplementation or which people don't. And the key reason for that is because people

[Ben]:

differ in very many different ways. And so by randomly allocating, rather than letting

[Ben]:

people decide for themselves if they want to have vitamin D, you're then essentially

[Ben]:

making the control group and the treatment group comparable. They will have similar characteristics.

[Ben]:

And so if you look and find differences in some kind of health outcome between the

[Ben]:

groups, you can then attribute that purely down to whether someone was in the treatment

[Ben]:

versus the control group because the groups are largely identical because of the

[Ben]:

randomization. Now,

[Ben]:

a very kind of obvious situation where you can't do that is with smoking, for example.

[Ben]:

So if you want to know what is the causal effect upon health of smoking, you can't come

[Ben]:

along and do a randomized control trial because if you really do suspect that it has

[Ben]:

negative health impacts, then that would be not a very friendly thing to do. And there

[Ben]:

are some things where you just literally, physically cannot randomly assign things. So

[Ben]:

you can't randomly assign people to be born in different countries, for example, unless

[Ben]:

you wanna get involved in large scale baby abduction, which I'm not too into.

[Alex]:

and which you are not advocating on this show

[Ben]:

No, not at this time. Yeah, and so what you have is randomized control trials are not

[Ben]:

infallible, but they're maybe the best go-to solution. But because there are many

[Ben]:

situations where you can't do randomization, you're then left in this situation where you

[Ben]:

can either say... I don't know how to make causal claims in this situation because

[Ben]:

of all these confounding variables. Or you can say, well, maybe there are clever things

[Ben]:

that I can do in order to try to make causal claims. And very often, you know, we're

[Ben]:

dealing with real world, messy data, many potential confounding variables. So many

[Ben]:

of these approaches rely upon making certain assumptions and sometimes those assumptions

[Ben]:

are realistic and you can convince yourself and others that that's the case. Other times

[Ben]:

not so much and maybe you can't always make causal claims. But the real reason why

[Ben]:

quasi-experiments are cool to know about is because very often in like real world scientific

[Ben]:

or industry applications, you will be in a situation where you didn't do any randomization

[Ben]:

and you can't do any randomization in the future. And so do you just throw your hands

[Ben]:

up in the air or do you start learning about quasi experiments and explore causal

[Ben]:

pi?

[Alex]:

Mm hmm. I see. Yeah. And so I think that's, that's pretty clear. So basically,

[Alex]:

any of those cases, folks, if that's something you are doing, definitely check out

[Alex]:

causal pie, because, well, that should help you at least jumpstart your analysis,

[Alex]:

because basically, as Ben was saying, it's kind of a toolbox of how to do these kind

[Alex]:

of experiments in PMC in the Bayesian framework and actually also if you want I think

[Alex]:

there is also some scikit-learn some scikit-learn models right bent but

[Ben]:

Yeah,

[Alex]:

mainly

[Ben]:

that's

[Alex]:

out

[Ben]:

right.

[Alex]:

of the box by default it's going to be a Bayesian solution but you can do some

[Alex]:

comparison with with some scikit-learn models if you want right

[Ben]:

Yeah, we've used a Trojan Horse kind of tactic very similar to how the software

[Ben]:

JASP implements both traditional OLS, Frequentist methods, but then also Bayesian

[Ben]:

stuff. So someone would be able to come along to CausalPy not really knowing much about

[Ben]:

Bayes and just run regular OLS type of approaches, but then if they wanted to... it

[Ben]:

would not be very much of a leap at all in order for them to basically just do the

[Ben]:

same analysis but from a Bayesian perspective.

[Alex]:

Yeah, I like that. I'm switching to Spanish now.

[Alex]:

Yeah, OK, so I think that's a good wrap up of CausalPy. And... I have so many questions

[Alex]:

for you because of inference, but we're going to stop there because I also want to

[Alex]:

talk about Piemcy marketing and maybe teaching. So yeah, Piemcy marketing, that's

[Alex]:

another open source package to which you've contributed. So can you talk to us about

[Alex]:

that? And mainly what's the difference between Piemcy marketing and

[Ben]:

Yeah, so I should definitely express the appropriate humility. So I have largely

[Ben]:

just been involved in the kind of peripheral aspects of PMC marketing, working on the docs

[Ben]:

and some of the kind of promotional activity that we've done. So we've had a whole bunch

[Ben]:

of people both inside PMC labs. and outside as well who have put a lot of time and effort

[Ben]:

into that. So I just want to make sure that, um, that's clear to everybody. Um, but

[Ben]:

Pimacy Marketing has a similar kind of ambition of making, uh, kind of quite complex

[Ben]:

and sophisticated analysis, um, more openly available to regular data scientists.

[Ben]:

Uh, so. This kind of evolved really out of work that PIMC Labs has done with a number

[Ben]:

of different companies now. So one of the great case studies that we've had was to

[Ben]:

work on media mix marketing, which maybe we'll talk about with HelloFresh. And we made

[Ben]:

some great improvements to an already great model that they had on that front. But

[Ben]:

in the meantime, since then, we've basically worked with many different companies, all interested

[Ben]:

in kind of marketing related questions. And what we felt was we would like to package

[Ben]:

these things up and make them kind of accessible. So the idea there is to just push

[Ben]:

the envelope a little bit and, yeah, see what happens. So I'd say it has. Two core

[Ben]:

pillars at the moment. The first

[Alex]:

Mm-hmm.

[Ben]:

one would be this media mix modeling component. And that's all about how effective

[Ben]:

is my advertising? The other pillar would be customer lifetime value. And that tries

[Ben]:

to address questions like, on a customer level, how valuable do I expect each of these

[Ben]:

people to be in terms of like ongoing revenue. Because then you can start making

[Ben]:

decisions like how much marketing money should you put in to begin with in order to

[Ben]:

acquire a customer? Because if your customers are very expensive to gain through marketing

[Ben]:

efforts, but you know that they have very low lifetime value, then that's not a

[Ben]:

winning business proposition. So. These two pillars, I think, are quite complementary.

[Ben]:

So that's my quick overview of PiMC marketing.

[Alex]:

yeah I like it so same for people interested we will put all the links in the show notes

[Alex]:

but basically that's the the quick overview and of course these two packages are open source

[Alex]:

so you can download them for free use them completely for free if you find bugs or

[Alex]:

have feature requests please open issues and even better pull requests Maybe, yeah,

[Alex]:

you've mentioned it a bit, MMMs, Ben, but do you have some examples of what people

[Alex]:

can do with PIMC marketing?

[Ben]:

Yeah, so the basic idea of a media mix model is to ask the question of, are my advertising

[Ben]:

dollars being spent wisely? So the kind of data set that you might be working with

[Ben]:

is a bunch of time series of how much money did we spend on marketing through social

[Ben]:

media? How much money did we spend over time through linear TV advertising and you

[Ben]:

may have many different advertising channels and there are lots now with different types

[Ben]:

of social media channels for example. Now if your advertising budgets can be very

[Ben]:

very large like multiple millions, tens if not hundreds of millions of dollars and

[Ben]:

so you don't really want to be just having a guess of how much money you should allocate

[Ben]:

to each of these advertising channels. So in the past it used to be relatively easy

[Ben]:

to work out how effective a different advertising channels because of things like

[Ben]:

user tracking and cookies and so on but with the kind of increased privacy, much of

[Ben]:

that information is going away. And so now the only real information you have is,

[Ben]:

how much did I spend? But then how many new customers did I acquire over time? Or how

[Ben]:

many new, how many products did I sell over time? And so media mix modeling essentially

[Ben]:

is fancy, multiple linear regression, which tries to say, how many customers or how

[Ben]:

many sales are being driven? by each channel. So that's at its basics. It's multiple

[Ben]:

linear regression. And then there are some fancy extensions that you can put on top

[Ben]:

of that in order to deal with more complicated aspects of how you think advertising works,

[Ben]:

how to deal with multiple different geographical regions or different categories of products,

[Ben]:

for example.

[Alex]:

yeah and that's something we've already talked a bit about already on the podcast Luciano

[Alex]:

pass with here to go through the different steps that we took for the Hellfresh model

[Alex]:

using MMMs also Ali Ellie McDonnell fit was here so I'm gonna I'm gonna link to

[Alex]:

these two episodes in the show notes for sure if people want to dig a bit deeper

[Alex]:

on how to use media mix marketing models but definitely super helpful and and the the

[Alex]:

cool thing of piemce marketing is that it has those models already under the hood

[Alex]:

so you can already do a an efficient first pass at least on these kind of analysis.

[Alex]:

Um, we're getting to the end of the show. So I'm actually wondering if like looking

[Alex]:

ahead, what future directions do you see for causal pie and PMC marketing and their

[Alex]:

integration in industry.

[Ben]:

Yeah, so I mean, there's so much I could say about future directions of CausalPy.

[Alex]:

Hmm

[Ben]:

So we definitely don't want to create an unwieldy beast that just tries to have a

[Ben]:

go at lots of different types of causal inference. So we do want to retain some focus.

[Ben]:

Um, but yeah, we want to expand these methods out. Um, so hopefully make it quite

[Ben]:

easy for people. to create their own kind of custom models. So we basically provide

[Ben]:

all the hard work in terms of causal inference, and then people can come along and

[Ben]:

create their own custom models. What I would really like to see is we're starting

[Ben]:

to see engagement by kind of individuals, data scientists, and companies now. And so

[Ben]:

one of the things that I'm really excited about is to basically... get engagement with

[Ben]:

those people and to learn how they are actually using CausalPy and then let that be

[Ben]:

a little bit demand driven. But we're an open source project, of course, so we already

[Ben]:

have a work in progress pull request which will add meta learning capabilities.

[Ben]:

So that's another causal inference method, which is a little bit distinct from what we've

[Ben]:

got so far. In terms of integration, sorry.

[Alex]:

Now yeah, maybe are you ready to talk about the do calculus operators that is under

[Alex]:

development for PyMC because it's related to CodlPy

[Ben]:

Yeah, so the do operator basically relates back to this idea of making an intervention

[Ben]:

that I mentioned before. And this is quite an important component when you're trying

[Ben]:

to analyze the causal effect of a certain intervention. And you can do this in PyMC right

[Ben]:

now. but it's maybe it does involve a slight kind of work around in order to make

[Ben]:

it work. So if people are interested in how you can do that, then we've got, well,

[Ben]:

you can look at the CausalPy code now and there are also a number of really clear

[Ben]:

examples on the PyMC examples repository that you can get through the PyMC website that

[Ben]:

show how to do it. But very soon, hopefully a matter of weeks, we will get an official

[Ben]:

pm.do operator. And this is gonna be really cool because what you can do is basically

[Ben]:

create a causal model, like a regular PMC model that you think describes your data

[Ben]:

generating process. You can then provide that the model into pm.do. and it will basically

[Ben]:

do graph surgery because PyMC has the concept of a symbolic graph and it will allow

[Ben]:

you to go into particular variables and intervene and set them to particular values.

[Ben]:

For example, is someone in a treatment condition or a control condition? And it will

[Ben]:

basically give you back out a mutilated graph which is the result of the do operation

[Ben]:

and then you can use that in order to basically make your interventional and counterfactual

[Ben]:

predictions. So that will be really cool when that comes out.

[Alex]:

Yeah, that's so cool. Can't wait to have that. And we love to see the, like, all

[Alex]:

of these coming together, right, for the PIMC environment, basically. That's pretty

[Alex]:

amazing. That's really awesome. So, yeah, thank you to all of you working on that.

[Alex]:

Of course, we'll definitely talk about that on the show. whether it is a classic episode

[Alex]:

or even I think would be even better would be a webinar format this format I just

[Alex]:

started where you like you the guest come on the show and share their screen and

[Alex]:

go through a model I think to demonstrate the dual operator that would be the the best

[Alex]:

thing so once it's in the package then you should probably come back to the show in

[Alex]:

a webinar and walk us through one or two models and people will ask you live questions.

[Alex]:

It'll show on your screen. And I think that would be the best format to introduce

[Alex]:

people to that operator and the kind of thinking and the kind of features that now

[Alex]:

will be available in Poem C. So yeah, and FYI, people, so those webinars are open

[Alex]:

to everybody. But you get to. at least a 50% discount on these webinars if you are

[Alex]:

a patron, a lot of you can just access them for free and you get access to the recording

[Alex]:

one month before everybody. So subscribe, support the show on Patreon, that helps a lot

[Alex]:

actually for the editing for a lot of stuff. And actually now we're recording the

[Alex]:

show on Riverside. And so that makes everything easier, especially for the guests.

[Alex]:

So I have to pay for Riverside. It is not cheap, but I find it makes the experience...

[Alex]:

way better for everybody. So thank you to all of you who are contributing every month,

[Alex]:

even if it's a small amount. I am eternally, eternally grateful. That helps not only for

[Alex]:

editing, but for making the whole show better. So again, thanks a lot to everybody.

[Alex]:

And oh yeah, Ben, I wanted to ask you about education a bit before closing up the

[Alex]:

show,

[Alex]:

We often teach workshops together, which is always a lot of fun. And you've actually

[Alex]:

taught very recently the first causal inference module that you developed very recently

[Alex]:

at the time of recording this podcast. And I'm curious, what would you say are the

[Alex]:

key skills to develop to start learning Bayesian stance?

[Ben]:

So if I were to go back in time and give myself advice, number one would be don't just

[Ben]:

buy the book and let it sit on your bookshelf. You actually have to dedicate time to engage

[Ben]:

with it.

[Alex]:

Hehehe

[Ben]:

But for all of you with more willpower than I have, I think one of the key things

[Ben]:

that works for me is to kind of really engage with whatever book and educational material

[Ben]:

you have. And what I mean by that is don't just kind of passively read but actually

[Ben]:

code along, experiment, work through the examples that you're given because that

[Ben]:

turns it very much into play and just in the same way that... how it was when I was

[Ben]:

learning BBC basic back when I was very young by experimenting and playing around.

[Ben]:

It allows you to see where you have gone wrong, where you understand correctly and where

[Ben]:

you have misunderstood things.

[Alex]:

Yeah, yeah, for sure. That's... I would second that and say that's definitely a very

[Alex]:

important skill to develop. And... Something like... Have you noticed any common biggest

[Alex]:

difficulty? uh, Beijing beginners face usually when you, you teach patients stance

[Alex]:

or more generally, what do you think the biggest obstacles in the Beijing workflow

[Alex]:

are currently.

[Ben]:

So,

[Ben]:

this might sound provocative, but it's not meant to be. I think one of the biggest

[Ben]:

challenges is the fact that people have been exposed to frequentist ideas for so long.

[Alex]:

Mm-hmm.

[Ben]:

That's not to say that they're wrong, but they do shape your thinking. And so there

[Ben]:

can be quite an intellectual twist that happens when you get exposed to... Bayesian

[Ben]:

ideas. So I think that just running simulations, simulating data, just using whatever programming

[Ben]:

language you have is probably a good way to kind of start making that that conceptual

[Ben]:

twist in your mind. Because once you see that oh, I can create a data generating

[Ben]:

process by using random numbers and having some variables depend on other ones, I think

[Ben]:

that really helps understand the Bayesian concepts. But yeah, in the past, I would have

[Ben]:

said that the rate limiting factor would have been availability of decent intro level

[Ben]:

books and the packages, but that is definitely not the case anymore. So there are tons of

[Ben]:

kind of really good... intro to intermediate level Bayesian resources out there. And just

[Ben]:

to kind of interpret your question a little bit more, I'd say that we're maybe just

[Ben]:

at the beginning of that similar transition with causality. So I think that there's lots

[Ben]:

of potential for packages, maybe like causal pie, but also educational material to

[Ben]:

make it much easier. for practitioners to get a grip on things.

[Alex]:

Yeah, that's true. I had not seen it like that, but that's a good point about where

[Alex]:

we are with causal inference also. For sure that's something very interesting and

[Alex]:

something I will be very curious also when we teach this material. more frequently

[Alex]:

in the workshops because that's definitely a topic that's very much on top of people's

[Alex]:

mind a lot this idea of interpretable models and white box models these seems to

[Alex]:

be very important for people which i understand i definitely share that concern

[Alex]:

so yeah cool maybe before Asking you the last two questions, looking ahead again,

[Alex]:

I'm wondering if you have some, like if there are some areas that you are most excited

[Alex]:

about for patient stats in the future, and what would you like to see and what you

[Alex]:

would like to not see?

[Ben]:

I think, um, kind of two things that I'm excited about, and this is not to say that

[Ben]:

I have any, um, like specialist knowledge or expertise in these areas, but, um, one thing

[Ben]:

that I'm really interested in is what it becomes possible in a probabilistic programming

[Ben]:

language when you have a language like Julia. So what I mean by that is, um, in order

[Ben]:

for... packages to do Bayesian inference, we need gradient information. And at the risk

[Ben]:

of just diving in a bit, in PyMC, we calculate gradient information by having a

[Ben]:

graph which allows you to do auto differentiation to calculate the gradients. And that's cool.

[Ben]:

but it does kind of mean if you want to do anything exotic, you have to learn a

[Ben]:

library that allows you to do that. So right now, the one that we're relying on is

[Ben]:

a pi tensor. Now in Julia, you still have to find gradient information if you want

[Ben]:

to do efficient Bayesian inference. But now the thing that is slightly different is

[Ben]:

that there are packages in Julia which allow you to do auto-differentiation on base-Julia

[Ben]:

code. And what that means is for the user, they don't then have to learn a kind

[Ben]:

of a custom package that does auto-differentiation like PyTensor. They could just learn base-Julia,

[Ben]:

write some kind of like reasonably arbitrary model. and then just run inference on it. So

[Ben]:

that to me is kind of really exciting in terms of making like relatively complicated

[Ben]:

models accessible quite easily.

[Ben]:

The other thing I'm interested in, and I think that... what PyMC is kind of making

[Ben]:

great progress here is doing operations on the graphs. So

[Alex]:

Yeah.

[Ben]:

all PPLs basically give you a log probability and hopefully gradient information, but not

[Ben]:

all of them have kind of like an underlying explicit graph structure. But when you do have

[Ben]:

that graph structure there, then what that means is you are able to go in and do

[Ben]:

surgery on the graphs. We've talked about one clear example of that with the do operator,

[Ben]:

where you can go in and replace a random variable with a constant basically, and cut

[Ben]:

nodes into this thing that you're intervening on. But other applications of this would be

[Ben]:

graph simplification. So in the case where you have conjugate priors, for example,

[Ben]:

you could presumably write some code to find situations where you can go in and

[Ben]:

massively simplify the graph and so minimize the amount of computation time that you have

[Ben]:

to do. So I don't do any of this stuff, I don't implement any of it, but those are

[Ben]:

two things that I'm excited about at the moment.

[Alex]:

Yep, I understand that that does sound super cool. Yeah, yeah, I love it. And that

[Alex]:

it's true that that limitation of where you have to learn another package all the time

[Alex]:

can can definitely be a limitation. And that's also why to basically have a loop

[Alex]:

of the whole show. developing that ability and that skill to fail comfortably and quickly

[Alex]:

is really important because then you are more comfortable learning things that make

[Alex]:

you very uncomfortable at the beginning. Any other topic that i didn't ask you about

[Alex]:

and that you'd like to mention?

[Ben]:

No, I think we've done a really good job at covering quite a lot of different things.

[Alex]:

Yeah, I think that was quite a lot. Thanks a lot for that comprehensive overview,

[Alex]:

Ben. So before letting you go, let me ask you the last two questions I ask every

[Alex]:

guest. That's better at the end of the show. So first one, if you had unlimited time

[Alex]:

and resources, which problem would you try to solve?

[Ben]:

Um, so I think it depends. Am I the only one who has unlimited time and resources or

[Ben]:

does do other

[Alex]:

Yeah.

[Ben]:

people?

[Alex]:

Um, let's say that's just you.

[Ben]:

Okay, so that puts quite a lot of pressure on me because

[Alex]:

Mm-hmm.

[Ben]:

that would suggest that I focus on the problems that will benefit the most people.

[Ben]:

So if that was true, I guess

[Ben]:

I'd have to kind of say something like creating a circular sustainable economy because

[Ben]:

as someone I don't know who likes to say, anything that's unsustainable will not

[Ben]:

be sustained. So zooming out, taking a kind of a sci-fi view or long term view of

[Ben]:

human history, we clearly need to live within the kind of energy and material boundaries

[Ben]:

of the planet. Otherwise things don't look so great.

[Alex]:

that is unfortunately I would say common answer to that question. The cool thing is

[Alex]:

that a lot of people answer that. So you folks can all have unlimited time and resources

[Alex]:

to work on that. Second question, if you could have dinner with any great scientific

[Alex]:

mind dead alive or fictional, who would it be?

[Ben]:

So, I think maybe because I don't have like a formal statistical training, I didn't

[Ben]:

feel like I had a great wealth of knowledge to really pluck someone out of the air. So

[Ben]:

I'm going to be slightly controversial in that I don't know if you could classify

[Ben]:

them as a scientist, but maybe the Buddha. I think that would be... interesting. You could

[Ben]:

claim that, you know, they were empirical because they're paying attention to what happens

[Ben]:

within one's mind, for example, and you could argue that they were experimental because

[Ben]:

they tried different approaches to life and meditation and kind of saw that some of

[Ben]:

those things didn't work and then moved on to a different approach. So I don't know

[Ben]:

if anyone's going to buy the fact that the Buddha might be a scientist. I'm not really

[Ben]:

sure. But I'm not really sure how much they would speak either to be honest. But I

[Ben]:

think it would be an interesting thing to write a blog post about.

[Alex]:

Yeah, probably they would ask you a lot of questions. Probably that. But definitely

[Alex]:

an interesting conversation. I like that answer. I would not say that qualifies

[Alex]:

as a scientific mind, but I do like the thought that you give to the question.

[Alex]:

It's definitely interesting. And that would make for a very interesting dinner for

[Alex]:

sure. so yeah thanks for that answer Ben you are definitely the first one to answer

[Alex]:

that

[Ben]:

Yeah. Great.

[Alex]:

okay awesome well I think that's it let's call this a show thanks a lot Ben I learned

[Alex]:

a lot honestly uh really love I'm really happy that uh we managed to uh to do that to

[Alex]:

do that episode so many things um as we said let's do a webinar once the do operator

[Alex]:

is out in poem see and will walk you folks through the through the webinar so that

[Alex]:

you can readily use that after what in your own modeling and other than that as

[Alex]:

usual I will put resources and a link to your website in the show notes for those

[Alex]:

who when I dig deeper and connect with you Thank you again, Ben, for taking the time

[Alex]:

and being on this show.

[Ben]:

Yeah, thank you very much for having me, it's fun.

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