CausalML Weekly

<p>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...

<p>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...

<p>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...

<p>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...

<p>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...

<p>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...

<p>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...

<p>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...

<p>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...

<p>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...

<p>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...

<p>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...

<p>This podcast, generated by NotebookLM, summarizes the Causal ML book by Victor Chernozhukov, Christian Hansen, Nathan Kallus, Martin Spindler, and Vasilis Syrgkanis.</p><p><br></p><p>Disclosure</p>The CausalML Book...