CausalML Book Ch17: Regression Discontinuity Designs in Causal Inference
Failed to add items
Sorry, we are unable to add the item because your shopping cart is already at capacity.
Add to basket failed.
Please try again later
Add to Wish List failed.
Please try again later
Remove from Wish List failed.
Please try again later
Follow podcast failed
Unfollow podcast failed
CausalML Book Ch17: Regression Discontinuity Designs in Causal Inference
-
Narrated by:
-
By:
About this listen
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
No reviews yet
In the spirit of reconciliation, Audible acknowledges the Traditional Custodians of country throughout Australia and their connections to land, sea and community. We pay our respect to their elders past and present and extend that respect to all Aboriginal and Torres Strait Islander peoples today.