Lihua Lei, Roshni Sahoo, Stefan Wager
arXiv 23 Apr 2023 · Econometrics · 4 citations (OpenAlex)
arXiv:2304.11735 · PDF · DOI · OpenAlex · Extracted main text
Practitioners often use data from a randomized controlled trial to learn a treatment assignment policy that can be deployed on a target population. A recurring concern in doing so is that, even if the randomized trial was well-executed (i.e., internal validity holds), the study participants may not represent a random sample of the target population (i.e., external validity fails)--and this may lead to policies that perform suboptimally on the target population. We consider a model where observable attributes can impact sample selection probabilities arbitrarily but the effect of unobservable attributes is bounded by a constant, and we aim to learn policies with the best possible performance guarantees that hold under any sampling bias of this type. In particular, we derive the partial identification result for the worst-case welfare in the presence of sampling bias and show that the optimal max-min, max-min gain, and minimax regret policies depend on both the conditional average treatment effect (CATE) and the conditional value-at-risk (CVaR) of potential outcomes given covariates. To avoid finite-sample inefficiencies of plug-in estimates, we further provide an end-to-end procedure for learning the optimal max-min and max-min gain policies that does not require the separate estimation of nuisance parameters.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Christopher Adjaho and Timothy Christensen (2022) Externally valid treatment choice | 0.693 | 7 | 1 | 100% |
| 2 | Charles F Manski (2011) Choosing treatment policies under ambiguity | 0.693 | 7 | 1 | 100% |
| 3 | Nian Si, Fan Zhang, Zhengyuan Zhou, and Jose Blanchet (2020) Distributional robust batch contextual bandits | 0.693 | 7 | 1 | 100% |
| 4 | Leonard J Savage (1951) The theory of statistical decision | 0.693 | 5 | 1 | 100% |
| 5 | Nathan Kallus and Angela Zhou (2021) Minimax-optimal policy learning under unobserved confounding | 0.644 | 4 | 1 | 100% |
| 6 | Tong Mu, Yash Chandak, Tatsunori B Hashimoto, and Emma Brunskill (2022) Factored DRO: Factored distributionally robust policies for contextual bandits | 0.644 | 4 | 1 | 100% |
| 7 | Roshni Sahoo, Lihua Lei, and Stefan Wager (2022) Learning from a biased sample self | 0.630 | 8 | 1 | 75% |
| 8 | Eli Ben-Michael, D James Greiner, Kosuke Imai, and Zhichao Jiang (2021) Safe policy learning through extrapolation: Application to pre-trial risk assessment | 0.585 | 3 | 1 | 100% |
| 9 | Alan S Gerber, Donald P Green, and Christopher W Larimer (2008) Social pressure and voter turnout: Evidence from a large-scale field experiment | 0.585 | 3 | 1 | 100% |
| 10 | Toru Kitagawa and Aleksey Tetenov (2018) Who should be treated? empirical welfare maximization methods for treatment choice | 0.585 | 3 | 1 | 100% |
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