CausalML Book Ch17: Regression Discontinuity Designs in Causal Inference
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CausalML Book Ch17: Regression Discontinuity Designs in Causal Inference
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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 determined by a running variable crossing a cutoff value. The text highlights how modern machine learning (ML) methods can enhance RDD analysis, particularly when dealing with numerous covariates, improving efficiency and allowing for the study of heterogeneous treatment effects. An empirical example demonstrates the application of RDD and ML techniques to analyze the impact of an antipoverty program in Mexico.
Disclosure
- The 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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