Summary of Off-policy Evaluation For Large Action Spaces Via Conjunct Effect Modeling, by Yuta Saito et al.
Off-Policy Evaluation for Large Action Spaces via Conjunct Effect Modeling
by Yuta Saito, Qingyang Ren, Thorsten Joachims
First submitted to arxiv on: 14 May 2023
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Artificial Intelligence (cs.AI); 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 OffCEM estimator addresses excessive variance issues in off-policy evaluation (OPE) for large discrete action spaces by employing the conjunct effect model (CEM). This novel approach decomposes the causal effect into a cluster effect and a residual effect, using importance weighting only on action clusters. The estimator is unbiased under the local correctness condition, which ensures that the residual-effect model preserves relative expected reward differences within each cluster. A two-step procedure minimizes bias in the first step and variance in the second step. Compared to conventional estimators, OffCEM provides substantial improvements in OPE, especially with many actions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We study how to evaluate whether a set of choices is good or not when we can’t control what happens next. This is important because it helps us learn from our past decisions without having to make all the same ones again. The problem gets harder when there are lots of options, but we propose a new way to deal with this by using a special model that breaks down the effect of each choice into two parts: one part depends on what others choose, and another part is unique to each option. We then use this model to make better guesses about how good or bad our past choices were. Our method works especially well when there are many options, and it can help us learn more accurately from our experiences. |
