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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 [ Ссылка ] !

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 [ Ссылка ] to unlock exclusive Bayesian swag ;)

Links from the show:

Ben’s website: [ Ссылка ]
Ben on GitHub: [ Ссылка ]
Ben on Twitter: [ Ссылка ]
Ben on LinkedIn: [ Ссылка ]
CausalPy – Causal inference for quasi-experiments: [ Ссылка ]
PyMC Marketing – Bayesian marketing toolbox in PyMC: [ Ссылка ]
PyMC Labs : [ Ссылка ]
LBS #23 – Bayesian Stats in Business and Marketing Analytics, with Elea McDonnel Feit: [ Ссылка ]
LBS #63 – Media Mix Models & Bayes for Marketing, with Luciano Paz: [ Ссылка ]

# Timestamps
00:00:00 Episode starts
00:01:36 How did you come into the world of statics and probabilistic modelling?
00:15:50 Can you define the work you are doing and also the topics you are particularly interested in?
00:19:23 Defining causality
00:21:47 Are there circumstances where causal inference is most helpful?
00:24:28 What are some of the challenges and limitations that practitioners face when doing causal inference currently?
00:32:09 Can you give us an overview of CausalPy and tell us how it tries to solve these challenges?
00:37:04 Is there any real-world applications of CausalPy that you would like to highlight?
00:40:27 Can you define quasi-experiments...
00:47:24 Can you tell us about PyMC Marketing? And what is the difference between PyMC Marketing and CausalPy
00:51:14 Do you have examples of what people could do with PyMC marketing?
00:56:34 The Do operator
01:03:45 Have you noticed any common difficulties Bayesian practitioners face?
01:12:24 If you had unlimited time and resources, which problem would you try to solve?
01:14:00 If you could have dinner with any great scientific mind dead, alive or fictional, who would it be?

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