PODCAST · technology
CausalML Weekly
by Jeong-Yoon Lee
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.
Episodes
-
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…
more -
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...
-
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...
-
CausalML Book Ch15: Causal Machine Learning: CATE Estimation and Validation
This episode focuses on methods for estimating and validating individualized treatment effects, particularly using machine learning (ML) techniques. It explores various "meta-learning" strategies like the S-Learner, T-Learner, Doubly Robust (DR)-Learner, and Residual (R)-Learner, comparing their...
-
CausalML Book Ch14: Statistical Inference on Heterogeneous Treatment Effects
This episode focuses on Conditional Average Treatment Effects (CATEs), which are crucial for understanding how treatments affect different subgroups. It contrasts CATEs with simpler average treatment effects, highlighting the complexity and importance of personalized policy decisions. The text...
-
CausalML Book Ch13: DML Inference Under Weak Identification
This episode explores advanced econometric methods for causal inference using Double/Debiased Machine Learning (DML). It focuses on applying DML to instrumental variable (IV) models, including partially linear IV models and interactive IV regression models (IRM) for estimating Local Average...
-
CausalML Book Ch12: Unobserved Confounders, Instrumental Variables, and Proxy Controls
This episode examines methods for causal inference when unobserved variables, known as confounders, complicate identifying true causal relationships. It begins by discussing sensitivity analysis to assess how robust causal inferences are to such unobserved confounders. The text then introduces...
-
CausalML Book Ch11: DAGs: Good and Bad Controls for Causal Inference
This episode focuses on causal inference and the selection of control variables within the framework of Directed Acyclic Graphs (DAGs). It explains various strategies for constructing valid adjustment sets to identify average causal effects, such as conditioning on parents or common causes of...
-
CausalML Book Ch10: Feature Engineering for Causal and Predictive Inference
This episode focuses on feature engineering, a technique that transforms complex data like text and images into numerical representations called embeddings for use in predictive and causal applications. It begins by explaining principal component analysis and autoencoders as methods for…
more -
CausalML Book Ch9: Statistical Inference in Nonlinear Regression Models
This episode focuses on Double/Debiased Machine Learning (DML) methods for statistical inference on predictive and causal effects in complex regression models. It introduces Neyman orthogonality and cross-fitting as key ingredients to mitigate bias in high-dimensional settings, providing...
-
CausalML Book Ch8: Modern Nonlinear Regression: Trees, Neural Networks, and Prediction Quality
This episode explores modern nonlinear regression methods crucial for predictive inference in causal analysis. It focuses on tree-based techniques like regression trees, random forests, and boosted trees, as well as neural networks and deep learning. The text discusses the theoretical guarantees…
more -
CausalML Book Ch7: Causal Inference with Directed Acyclic Graphs and SEMs
This episode explores causal inference through the lens of directed acyclic graphs (DAGs) and nonlinear structural equation models (SEMs). It highlights how these models provide a formal, nonparametric framework for understanding causal relationships, moving beyond simpler linear assumptions. The...
-
CausalML Book Ch6: Causal Inference via Linear Structural Equations
This episode introduces linear structural equation models (SEMs) and causal diagrams, also known as Directed Acyclic Graphs (DAGs). The text explains how these models can be used for causal inference, particularly in economics, using examples like gasoline demand and wage gap analysis. It...
-
CausalML Book Ch5: Causal Inference: Conditional Ignorability and Propensity Scores
This episode focuses on methods for identifying average causal effects in observational studies. It explores the concept of conditional ignorability, explaining how adjusting for observed covariates can help mitigate selection bias, making non-randomized data comparable to randomized control...
-
CausalML Book Ch4: High-Dimensional Linear Regression and Causal Effects
This episode focuses on high-dimensional linear regression models, specifically discussing causal effects and inference methods. The core of the text explains the Double Lasso procedure, a technique utilizing Lasso regression twice to estimate predictive effects and construct confidence…
more -
CausalML Book Ch3: Predictive Inference with High-Dimensional Linear Regression
This episode focuses on predictive inference using linear regression methods in high-dimensional settings where the number of predictors (p) often exceeds the number of observations (n). The text primarily explores Lasso regression, explaining its mechanism for variable selection and reducing...
-
CausalML Book Ch2: Causal Inference Through Randomized Experiments
This episode provides a comprehensive overview of causal inference using Randomized Controlled Trials (RCTs), often considered the gold standard in establishing cause-and-effect relationships. The text begins by explaining the potential outcomes framework and the concept of Average Treatment...
-
CausalML Book Summary
This podcast, generated by NotebookLM, summarizes the Causal ML book by Victor Chernozhukov, Christian Hansen, Nathan Kallus, Martin Spindler, and Vasilis Syrgkanis. Disclosure The CausalML Book: Chernozhukov, V. & Hansen, C. & Kallus, N. & Spindler, M., & Syrgkanis, V. (2024): Applied Causal...
We're indexing this podcast's transcripts for the first time — this can take a minute or two. We'll show results as soon as they're ready.
No matches for "" in this podcast's transcripts.
No topics indexed yet for this podcast.
Loading reviews...
ABOUT THIS SHOW
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.
HOSTED BY
Jeong-Yoon Lee
CATEGORIES
Loading similar podcasts...
Frequently Asked Questions
How many episodes does CausalML Weekly have?
CausalML Weekly currently has 18 episodes available on PodParley. New episodes are automatically indexed when they're published to the podcast feed.
What is CausalML Weekly about?
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...
How often does CausalML Weekly release new episodes?
CausalML Weekly has 18 episodes. Check the episode list to see recent publication dates and frequency.
Where can I listen to CausalML Weekly?
You can listen to CausalML Weekly on PodParley by clicking any episode. We provide an embedded audio player for direct listening, and you can also subscribe via your preferred podcast app using the RSS feed.
Who hosts CausalML Weekly?
CausalML Weekly is created and hosted by Jeong-Yoon Lee.
URL copied to clipboard!