Intro

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*What can we learn about causal inference from the “war” between Bayesians and frequentists?*

What can we learn about causal inference from the “war” between Bayesians and frequentists?

In the episode, we cover:

- What can we learn from the “war” between Bayesians and frequentists?
- Why do Bayesian Additive Regression Trees (BART) “just work”?
- Do heterogeneous treatment effects exist?
- Is RCT generalization a heterogeneity problem?

In the episode, we accidentally coined a new term: “feature-level selection bias.”

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Video version available on the Youtube:

 https://youtu.be/-hRS8eU3Tow
Recorded in Arizona, US.

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*About The Guest*
Professor Richard Hahn, PhD, is a professor of statistics at Arizona State University (ASU). He develops novel statistical methods for analyzing data arising from the social sciences, including psychology, economics, education, and business. His current focus revolves around causal inference using regression tree models, as well as foundational issues in Bayesian statistics.

Connect with Richard:
- Richard on LinkedIn: https://www.linkedin.com/in/richard-hahn-a1096050/
- Richard's web page: https://methodologymatters.substack.com/about

*About The Host*
Aleksander (Alex) Molak is an independent machine learning researcher, educator, entrepreneur and a best-selling author in the area of causality (https://amzn.to/3QhsRz4 ).

Connect with Alex:
- Alex on the Internet: https://bit.ly/aleksander-molak

*Links*

Repo

- https://stochtree.ai

Papers

- Hahn et al (2020) - "Bayesian Regression Tree Models for Causal Inference" (https://projecteuclid.org/journals/bayesian-analysis/volume-15/issue-3/Bayesian-Regression-Tree-Models-for-Causal-Inference--Regularization-Confounding/10.1214/19-BA1195.full)

- Yeager, ..., Dweck et al (2019) - "A national experiment reveals where a growth mindset improves achievement" (https://www.nature.com/articles/s41586-019-1466-y)

- Herren, Hahn, et al (20

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