CausalML Weekly
Episodes
CausalML Book Ch1: Foundations of Linear Regression and Prediction
01 Jul 2025
Contributed by Lukas
This episode explores the foundational concepts of linear regression as a tool for predictive inference and association analysis. It details the Best ...
CausalML Book Ch17: Regression Discontinuity Designs in Causal Inference
01 Jul 2025
Contributed by Lukas
This episode explores a powerful method for identifying causal effects in non-experimental settings. The authors, affiliated with various universiti...
CausalML Book Ch16: Causal Inference with Difference-in-Differences and DML
01 Jul 2025
Contributed by Lukas
This episode introduces and explains the Difference-in-Differences (DiD) framework, a widely used method in social sciences for estimating causal eff...
CausalML Book Ch15: Causal Machine Learning: CATE Estimation and Validation
01 Jul 2025
Contributed by Lukas
This episode focuses on methods for estimating and validating individualized treatment effects, particularly using machine learning (ML) techniques....
CausalML Book Ch14: Statistical Inference on Heterogeneous Treatment Effects
01 Jul 2025
Contributed by Lukas
This episode focuses on Conditional Average Treatment Effects (CATEs), which are crucial for understanding how treatments affect different subgroup...
CausalML Book Ch13: DML Inference Under Weak Identification
01 Jul 2025
Contributed by Lukas
This episode explores advanced econometric methods for causal inference using Double/Debiased Machine Learning (DML). It focuses on applying DML to ...
CausalML Book Ch12: Unobserved Confounders, Instrumental Variables, and Proxy Controls
01 Jul 2025
Contributed by Lukas
This episode examines methods for causal inference when unobserved variables, known as confounders, complicate identifying true causal relationship...
CausalML Book Ch11: DAGs: Good and Bad Controls for Causal Inference
30 Jun 2025
Contributed by Lukas
This episode focuses on causal inference and the selection of control variables within the framework of Directed Acyclic Graphs (DAGs). It explains ...
CausalML Book Ch10: Feature Engineering for Causal and Predictive Inference
30 Jun 2025
Contributed by Lukas
This episode focuses on feature engineering, a technique that transforms complex data like text and images into numerical representations called emb...
CausalML Book Ch9: Statistical Inference in Nonlinear Regression Models
30 Jun 2025
Contributed by Lukas
This episode focuses on Double/Debiased Machine Learning (DML) methods for statistical inference on predictive and causal effects in complex regress...
CausalML Book Ch8: Modern Nonlinear Regression: Trees, Neural Networks, and Prediction Quality
30 Jun 2025
Contributed by Lukas
This episode explores modern nonlinear regression methods crucial for predictive inference in causal analysis. It focuses on tree-based techniques l...
CausalML Book Ch7: Causal Inference with Directed Acyclic Graphs and SEMs
30 Jun 2025
Contributed by Lukas
This episode explores causal inference through the lens of directed acyclic graphs (DAGs) and nonlinear structural equation models (SEMs). It highligh...
CausalML Book Ch6: Causal Inference via Linear Structural Equations
30 Jun 2025
Contributed by Lukas
This episode introduces linear structural equation models (SEMs) and causal diagrams, also known as Directed Acyclic Graphs (DAGs). The text explains ...
CausalML Book Ch5: Causal Inference: Conditional Ignorability and Propensity Scores
30 Jun 2025
Contributed by Lukas
This episode focuses on methods for identifying average causal effects in observational studies. It explores the concept of conditional ignorabilit...
CausalML Book Ch4: High-Dimensional Linear Regression and Causal Effects
30 Jun 2025
Contributed by Lukas
This episode focuses on high-dimensional linear regression models, specifically discussing causal effects and inference methods. The core of the tex...
CausalML Book Ch3: Predictive Inference with High-Dimensional Linear Regression
30 Jun 2025
Contributed by Lukas
This episode focuses on predictive inference using linear regression methods in high-dimensional settings where the number of predictors (p) often exc...
CausalML Book Ch2: Causal Inference Through Randomized Experiments
30 Jun 2025
Contributed by Lukas
This episode provides a comprehensive overview of causal inference using Randomized Controlled Trials (RCTs), often considered the gold standard in es...
CausalML Book Summary
30 Jun 2025
Contributed by Lukas
This podcast, generated by NotebookLM, summarizes the Causal ML book by Victor Chernozhukov, Christian Hansen, Nathan Kallus, Martin Spindler, and Vas...