Generalizing causal effects with noncompliance: Application to deep canvassing experiments

TL;DR

This paper develops new methods to estimate the causal impact of interventions on compliers in different populations, addressing limitations of existing IV approaches, and demonstrates their effectiveness through simulations and a real-world study on deep canvassing.

Contribution

It introduces a population-level exclusion restriction for identifying the target complier average causal effect without principal ignorability assumptions.

Findings

New identification assumptions enable estimation of T-CACE in diverse settings.

Proposed inverse-weighted estimators have desirable asymptotic properties.

Sensitivity analysis assesses robustness against unmeasured confounding.

Abstract

Standard approaches in generalizability often focus on generalizing the intent-to-treat (ITT). However, in practice, a more policy-relevant quantity is the generalized impact of an intervention across compliers. While instrumental variable (IV) methods are commonly used to estimate the complier average causal effect (CACE) within samples, standard approaches cannot be applied to a target population with a different distribution from the study sample. This paper makes several key contributions. First, we introduce a new set of identifying assumptions in the form of a population-level exclusion restriction that allows for identification of the target complier average causal effect (T-CACE) in both randomized experiments and observational studies. This allows researchers to identify the T-CACE without relying on standard principal ignorability assumptions. Second, we propose a class of inverse-weighted estimators for the T-CACE and derive their asymptotic properties. We provide extensions for settings in which researchers have access to auxiliary compliance information across the target population. Finally, we introduce a sensitivity analysis for researchers to evaluate the robustness of the estimators in the presence of unmeasured confounding. We illustrate our proposed method through extensive simulations and a study evaluating the impact of deep canvassing on reducing exclusionary attitudes.