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This podcast is available on 4 platforms including Apple Podcasts, Spotify, and more. You can also use the RSS feed directly.

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CausalML Weekly

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by Jeong-Yoon Lee

5.0(1 reviews)

18 episodes

Updated Daily

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Podcast Overview

Welcome to CausalML Weekly, the podcast where data meets decision-making. Join us as we explore the intersection of causal inference, machine learning, and real-world applications. This show will break down cutting-edge methods, foundational theory, and practical deployment of causal models. In each episode, we distill insights from influential literature, summarize complex topics with clarity, and sometimes bring on experts to discuss how causal inference is transforming industries—from uplift modeling and A/B testing to policy evaluation and personalized treatment strategies.

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6/30/2025

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Recent Episodes

July 1, 2025

CausalML Book Ch1: Foundations of Linear Regression and Prediction

This episode explores the foundational concepts of linear regression as a tool for predictive inference and association analysis. It details the Best Linear Prediction (BLP) problem and its finite-sample counterpart, Ordinary Least Squares (OLS), emphasizing their statistical properties, including analysis of variance and the challenges of overfitting when the number of parameters is not small relative to the sample size. The text further introduces sample splitting as a method for robustly evaluating predictive models and clarifies how partialling-out helps in understanding the predictive effects of specific regressors, such as in analyzing wage gaps. Finally, it discusses adaptive statistical inference and the behavior of OLS in high-dimensional settings where traditional assumptions may not hold.Disclosure<ul><li>The CausalML Book: Chernozhukov, V. & Hansen, C. & Kallus, N. & Spindler, M., & Syrgkanis, V. (2024): Applied Causal Inference Powered by ML and AI. CausalML-book.org; arXiv:2403.02467. </li><li>Audio summary is generated by Google NotebookLM https://notebooklm.google/</li><li>The episode art is generated by OpenAI ChatGPT</li></ul>

July 1, 2025

CausalML Book Ch17: Regression Discontinuity Designs in Causal Inference

This episode explores a powerful method for identifying causal effects in non-experimental settings. The authors, affiliated with various universities, explain the basic RDD framework, where treatment assignment is determined by a running variable crossing a cutoff value. The text highlights how modern machine learning (ML) methods can enhance RDD analysis, particularly when dealing with numerous covariates, improving efficiency and allowing for the study of heterogeneous treatment effects. An empirical example demonstrates the application of RDD and ML techniques to analyze the impact of an antipoverty program in Mexico.Disclosure<ul><li>The CausalML Book: Chernozhukov, V. & Hansen, C. & Kallus, N. & Spindler, M., & Syrgkanis, V. (2024): Applied Causal Inference Powered by ML and AI. CausalML-book.org; arXiv:2403.02467. </li><li>Audio summary is generated by Google NotebookLM https://notebooklm.google/</li><li>The episode art is generated by OpenAI ChatGPT</li></ul>

July 1, 2025

CausalML Book Ch16: Causal Inference with Difference-in-Differences and DML

This episode introduces and explains the Difference-in-Differences (DiD) framework, a widely used method in social sciences for estimating causal effects in situations with treatment and control groups over multiple time periods. It elaborates on the core assumption of "parallel trends" and discusses how Debiased Machine Learning (DML) methods can be used to incorporate high-dimensional control variables, enhancing the robustness of DiD analysis. The text illustrates these concepts with a practical example applying DML to study the impact of minimum wage changes on teen employment, analyzing different machine learning models and assessing their performance. The authors also briefly touch on more advanced DiD settings, such as those involving repeated cross-sections, and provide exercises for further study.Disclosure<ul><li>The CausalML Book: Chernozhukov, V. & Hansen, C. & Kallus, N. & Spindler, M., & Syrgkanis, V. (2024): Applied Causal Inference Powered by ML and AI. CausalML-book.org; arXiv:2403.02467. </li><li>Audio summary is generated by Google NotebookLM https://notebooklm.google/</li><li>The episode art is generated by OpenAI ChatGPT</li></ul>

18 total episodes available

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Frequently asked questions

Have a different question and can't find the answer you're looking for? Reach out to our support team by sending us an email and we'll get back to you as soon as we can.

What is CausalML Weekly?: Welcome to CausalML Weekly, the podcast where data meets decision-making.

Join us as we explore the intersection of causal inference, machine learning, and real-world applications. This show will break down cutting-edge methods, foundational theory, and practical deployment of causal models.

In each episode, we distill insights from influential literature, summarize complex topics with clarity, and sometimes bring on experts to discuss how causal inference is transforming industries—from uplift modeling and A/B testing to policy evaluation and personalized treatment strategies.
How often does this podcast release new episodes?: This podcast updates daily.
Where can I listen to this podcast?: This podcast is available on 4 platforms including Apple Podcasts, Spotify, and more. You can also use the RSS feed directly.
Does this podcast accept guests?: No, this podcast does not typically feature guests.

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