Xinkun Nie, Guido Imbens, Stefan Wager
arXiv 9 Dec 2021 · Econometrics · 6 citations (OpenAlex)
arXiv:2112.04723 · PDF · DOI · OpenAlex · Extracted main text
The ability to generalize experimental results from randomized control trials (RCTs) across locations is crucial for informing policy decisions in targeted regions. Such generalization is often hindered by the lack of identifiability due to unmeasured effect modifiers that compromise direct transport of treatment effect estimates from one location to another. We build upon sensitivity analysis in observational studies and propose an optimization procedure that allows us to get bounds on the treatment effects in targeted regions. Furthermore, we construct more informative bounds by balancing on the moments of covariates. In simulation experiments, we show that the covariate balancing approach is promising in getting sharper identification intervals.
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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 | Qingyuan Zhao, Dylan S Small, and Bhaswar B Bhattacharya (2019) Sensitivity analysis for inverse probability weighting estimators via the percentile bootstrap | 1.000 | 5 | 3 | 100% |
| 2 | Peter M Aronow and Donald KK Lee (2012) Interval estimation of population means under unknown but bounded probabilities of sample selection | 0.737 | 3 | 2 | 100% |
| 3 | V Joseph Hotz, Guido W Imbens, and Julie H Mortimer (2005) Predicting the efficacy of future training programs using past experiences at other locations self | 0.737 | 3 | 2 | 100% |
| 4 | Kosuke Imai and Marc Ratkovic (2014) Covariate balancing propensity score | 0.737 | 3 | 2 | 100% |
| 5 | Luke W Miratrix, Stefan Wager, and Jose R Zubizarreta (2017) Shape-constrained partial identification of a population mean under unknown probabilities of sample selection self | 0.737 | 3 | 2 | 100% |
| 6 | Paul R Rosenbaum (2002) Observational studies, volume 10 | 0.737 | 3 | 2 | 100% |
| 7 | Masashi Sugiyama, Taiji Suzuki, and Takafumi Kanamori (2012) Density ratio estimation in machine learning | 0.737 | 3 | 2 | 100% |
| 8 | Steve Yadlowsky, Hongseok Namkoong, Sanjay Basu, John Duchi, and Lu… (2018) Bounds on the conditional and average treatment effect in the presence of unobserved confounders | 0.737 | 3 | 2 | 100% |
| 9 | Guido W Imbens (2003) Sensitivity to exogeneity assumptions in program evaluation self | 0.644 | 2 | 2 | 100% |
| 10 | Qingyuan Zhao (2019) Covariate balancing propensity score by tailored loss functions | 0.644 | 2 | 2 | 100% |
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