Apoorva Lal, Wenjing Zheng, Simon Ejdemyr
arXiv 12 Jan 2023 · Statistics — Methodology
arXiv:2301.04776 · PDF · DOI · OpenAlex · Extracted main text
Randomized experiments are an excellent tool for estimating internally valid causal effects with the sample at hand, but their external validity is frequently debated. While classical results on the estimation of Population Average Treatment Effects (PATE) implicitly assume random selection into experiments, this is typically far from true in many medical, social-scientific, and industry experiments. When the experimental sample is different from the target sample along observable or unobservable dimensions, experimental estimates may be of limited use for policy decisions. We begin by decomposing the extrapolation bias from estimating the Target Average Treatment Effect (TATE) using the Sample Average Treatment Effect (SATE) into covariate shift, overlap, and effect modification components, which researchers can reason about in order to diagnose the severity of extrapolation bias. Next, We cast covariate shift as a sample selection problem and propose estimators that re-weight the doubly-robust scores from experimental subjects to estimate treatment effects in the overall sample (=: generalization) or in an alternate target sample (=: transportation). We implement these estimators in the open-source R package causalTransportR and illustrate its performance in a simulation study and discuss diagnostics to evaluate its performance.
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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 | Bia, Michela, Huber, Martin, Lafférs, Lukáš (2020) Double machine learning for sample selection models | 0.405 | 1 | 1 | 100% |
| 2 | Dahabreh, Issa J, Robertson, Sarah E, Tchetgen, Eric J, Stuart, Eliz… (2019) Generalizing causal inferences from individuals in randomized trials to all trial-eligible individuals | 0.405 | 1 | 1 | 100% |
| 3 | Dahabreh, Issa J, Robertson, Sarah E, Steingrimsson, Jon A, Stuart,… (2020) Extending inferences from a randomized trial to a new target population | 0.405 | 1 | 1 | 100% |
| 4 | Ding, Peng, Feller, Avi, Miratrix, Luke (2019) Decomposing Treatment Effect Variation | 0.405 | 1 | 1 | 100% |
| 5 | Hainmueller, Jens (2012) Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies | 0.405 | 1 | 1 | 100% |
| 6 | Kennedy, Edward H (2022) Semiparametric doubly robust targeted double machine learning: a review | 0.405 | 1 | 1 | 100% |
| 7 | Knaus, Michael C, Lechner, Michael, Strittmatter, Anthony (2020) Machine learning estimation of heterogeneous causal effects: Empirical Monte Carlo evidence | 0.405 | 1 | 1 | 100% |
| 8 | Nie, Xinkun, Imbens, Guido, Wager, Stefan (2021) Covariate Balancing Sensitivity Analysis for Extrapolating Randomized Trials across Locations | 0.405 | 1 | 1 | 100% |
| 9 | Athey, Susan, Bickel, Peter J, Chen, Aiyou, Imbens, Guido, Pollmann,… (2021) Semiparametric Estimation of Treatment Effects in Randomized Experiments | 0.405 | 1 | 1 | 100% |
| 10 | Chernozhukov, Victor, Demirer, Mert, Duflo, Esther, Fernández-Val, I… (2020) Generic Machine Learning Inference on Heterogenous Treatment Effects in Randomized Experiments | 0.405 | 1 | 1 | 100% |
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