mlsanityData to 13 Oct 2026

Externally Valid Policy Evaluation Combining Trial and Observational Data

arXiv 2310.14763 ↗First posted 23 Oct 2023stat.ME cs.LG stat.MLcited by 0

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Abstract (arXiv)

Randomized trials are widely considered as the gold standard for evaluating the effects of decision policies. Trial data is, however, drawn from a population which may differ from the intended target population and this raises a problem of external validity (aka. generalizability). In this paper we seek to use trial data to draw valid inferences about the outcome of a policy on the target population. Additional covariate data from the target population is used to model the sampling of individuals in the trial study. We develop a method that yields certifiably valid trial-based policy evaluations under any specified range of model miscalibrations. The method is nonparametric and the validity is assured even with finite samples. The certified policy evaluations are illustrated using both simulated and real data.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 17 of its 57 references that are in the map. First posted on arXiv 23 Oct 2023.

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References · 17

  1. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,143
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  11. 2021Off-Policy Risk Assessment in Contextual Banditscited by 29
  12. 2020Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confoundingcited by 25
5 more
  1. 2018Flexible sensitivity analysis for observational studies without observable implicationscited by 18
  2. 2021Covariate Balancing Sensitivity Analysis for Extrapolating Randomized Trials across Locationscited by 8
  3. 2022Sensitivity Analysis in the Generalization of Experimental Resultscited by 5
  4. 2021Generalizing trial evidence to target populations in non-nested designs: Applications to AIDS clinical trialscited by 3
  5. 2023Off-Policy Evaluation with Out-of-Sample Guaranteescited by 2

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