Summary of Structure-agnostic Optimality Of Doubly Robust Learning For Treatment Effect Estimation, by Jikai Jin and Vasilis Syrgkanis
Structure-agnostic Optimality of Doubly Robust Learning for Treatment Effect Estimation
by Jikai Jin, Vasilis Syrgkanis
First submitted to arxiv on: 22 Feb 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Econometrics (econ.EM); Statistics Theory (math.ST); Methodology (stat.ME)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel framework for estimating average treatment effects, a crucial problem in causal inference with applications across disciplines. By leveraging recent advancements in statistical lower bounds, the authors prove the optimality of widely used doubly robust estimators for both Average Treatment Effect (ATE) and Average Treatment Effect on the Treated (ATT), as well as weighted variants relevant to policy evaluation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding a way to accurately measure how different groups would react if they received different treatments. It’s important because it helps us understand what happens when we try to change something, like a medicine or a policy. The researchers used a new way of thinking about statistics to show that some popular methods for doing this are actually the best you can do. |
Keywords
* Artificial intelligence * Inference
