CausalML Weekly

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), emp... more

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 cr... more

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... more

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)-Lear... more

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 importanc... more

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 ... more

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 unob... more

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 ... more