CausalML Book Summary

CausalML Weekly
30/06/2025 12 min Temporada 1 Episodio 1

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Episode Synopsis

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: Chernozhukov, V. & Hansen, C. & Kallus, N. & Spindler, M., & Syrgkanis, V. (2024): Applied Causal Inference Powered by ML and AI. CausalML-book.org; arXiv:2403.02467. Audio summary is generated by Google NotebookLM https://notebooklm.google/The episode art is generated by OpenAI ChatGPT

More episodes of the podcast CausalML Weekly

CausalML Book Ch1: Foundations of Linear Regression and Prediction 01/07/2025
CausalML Book Ch17: Regression Discontinuity Designs in Causal Inference 01/07/2025
CausalML Book Ch16: Causal Inference with Difference-in-Differences and DML 01/07/2025
CausalML Book Ch15: Causal Machine Learning: CATE Estimation and Validation 01/07/2025
CausalML Book Ch14: Statistical Inference on Heterogeneous Treatment Effects 01/07/2025
CausalML Book Ch13: DML Inference Under Weak Identification 01/07/2025
CausalML Book Ch12: Unobserved Confounders, Instrumental Variables, and Proxy Controls 01/07/2025
CausalML Book Ch11: DAGs: Good and Bad Controls for Causal Inference 30/06/2025
CausalML Book Ch10: Feature Engineering for Causal and Predictive Inference 30/06/2025
CausalML Book Ch9: Statistical Inference in Nonlinear Regression Models 30/06/2025
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