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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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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,…
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…
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…
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…
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…
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…
S1E1 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.DisclosureThe CausalML Book:…
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CausalML Weekly has published 18 episodes since June 2025, covering topics in Technology.
CausalML Weekly is currently dormant with new episodes hourly. Average episode length is 19m.
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