CausalML Book Ch3: Predictive Inference with High-Dimensional Linear Regression episode artwork

EPISODE · Jun 30, 2025 · 37 MIN

CausalML Book Ch3: Predictive Inference with High-Dimensional Linear Regression

from CausalML Weekly · host Jeong-Yoon Lee

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 regression, explaining its mechanism for variable selection and reducing overfitting by penalizing coefficient magnitudes. It also compares Lasso to other penalized regression techniques like Ridge, Elastic Net, and Lava, discussing their suitability for different data structures such as sparse, dense, or sparse+dense coefficient vectors, and emphasizes the importance of cross-validation for selecting optimal tuning parameters.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

Episode metadata supplied by the publisher feed · Published Jun 30, 2025

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 regression, explaining its mechanism for variable selection and reducing overfitting by penalizing coefficient magnitudes. It also compares Lasso to other penalized regression techniques like Ridge, Elastic Net, and Lava, discussing their suitability for different data structures such as sparse, dense, or sparse+dense coefficient vectors, and emphasizes the importance of cross-validation for selecting optimal tuning parameters.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

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CausalML Book Ch3: Predictive Inference with High-Dimensional Linear Regression

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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 regression, explaining its...

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