Summary of Logarithmic Neyman Regret For Adaptive Estimation Of the Average Treatment Effect, by Ojash Neopane et al.
Logarithmic Neyman Regret for Adaptive Estimation of the Average Treatment Effect
by Ojash Neopane, Aaditya Ramdas, Aarti Singh
First submitted to arxiv on: 21 Nov 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 proposed Clipped Second Moment Tracking (ClipSMT) algorithm is designed to adaptively select treatment allocation probabilities to improve Estimation of Average Treatment Effect (ATE) in causal inference and Off-Policy Evaluation. Building upon an existing algorithm with strong asymptotic optimality guarantees, ClipSMT provides finite sample bounds on its Neyman regret. The algorithm achieves exponential improvements in Neyman regret by reducing the dependence on T from O(sqrt(T)) to O(log T) and polynomially scaling problem parameters. This is significant as existing non-asymptotic methods suffer from poor empirical performance and exponential scaling. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at a way to improve estimation of Average Treatment Effect (ATE). Right now, most methods focus on how well they work when the sample size gets really big. However, this doesn’t help with real-world problems where we don’t have that many data points. The researchers came up with an algorithm called ClipSMT that can adaptively choose treatment allocation probabilities to better estimate ATE. This is important because it could lead to more accurate results and better decision-making. |
Keywords
» Artificial intelligence » Inference » Tracking
