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Hosted by Jeong-Yoon Lee · 🇺🇸 US · EN · 18 episodes
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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.
Jeong-Yoon Lee hosts CausalML Weekly, a technology show with 18 episodes published.
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, Ord
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 deter
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
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-Le
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, highlightin
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
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 caus
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 av
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 expl
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
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 neur
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 unde
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 eco
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 sel
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 regre
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
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 explaini
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. & Hanse
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CausalML Weekly is hosted by Jeong-Yoon Lee. The show is categorised under technology and has published 18 episodes.
CausalML Weekly has published 18 episodes.
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