Primary Submission Category: Generalizability/Transportability
Generalizing Causal Effects with Noncompliance
Authors: Zhongren Chen, Melody Huang,
Presenting Author: Zhongren Chen*
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 experimental 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. Third, we introduce an optimization-based sensitivity analysis framework to assess the robustness of the estimator in the presence of unmeasured confounding. Our extensive simulations demonstrate that the proposed estimator yields low bias and achieves accurate coverage of confidence intervals. We illustrate our proposed method on a study evaluating the impact of deep canvassing on reducing exclusionary attitudes.