Covariate Balancing Sensitivity Analysis for Extrapolating Randomized Trials across Locations

TL;DR

This paper introduces a covariate balancing sensitivity analysis method to improve the extrapolation of randomized trial results across different locations, addressing unmeasured effect modifiers and providing sharper treatment effect bounds.

Contribution

It develops an optimization-based approach that balances covariate moments to obtain more informative bounds on treatment effects in new regions.

Findings

Covariate balancing yields sharper bounds in simulations.

The method effectively addresses unmeasured effect modifiers.

Bounds improve the reliability of extrapolated treatment effects.

Abstract

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.