A Framework for Generalization and Transportation of Causal Estimates Under Covariate Shift

Apoorva Lal, Wenjing Zheng and Simon Ejdemyr

Papers from arXiv.org

Abstract: 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.

Date: 2023-01
New Economics Papers: this item is included in nep-ecm and nep-exp
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://arxiv.org/pdf/2301.04776 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2301.04776

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().