Two-Phase Treatment with Noncompliance: Identifying the Cumulative Average Treatment Effect via Multisite Instrumental Variables
By: Guanglei Hong , Xu Qin , Zhengyan Xu and more
Potential Business Impact:
Helps measure treatment effects even with tricky behavior.
When evaluating a two-phase intervention, the cumulative average treatment effect (ATE) is often the primary causal estimand of interest. However, some individuals who do not respond well to the Phase I treatment may subsequently display noncompliant behaviours. At the same time, exposure to the Phase I treatment is expected to directly influence an individual's potential outcomes, thereby violating the exclusion restriction. Building on an instrumental variable (IV) strategy for multisite trials, we clarify the conditions under which the cumulative ATE of a two-phase treatment can be identified by employing the random assignment of the Phase I treatment as the instrument. Our strategy relaxes both the conventional exclusion restriction and sequential ignorability assumptions. We assess the performance of the new strategy through simulation studies. Additionally, we reanalyze data from the Tennessee class size study, in which students and teachers were randomly assigned to either small or regular class types in kindergarten (Phase I) with noncompliance emerging in Grade 1 (Phase II). Applying our new strategy, we estimate the cumulative ATE of receiving two consecutive years of instruction in a small versus regular class.
Similar Papers
Estimating the Complier Average Causal Effect in Randomised Controlled Trials with Non-Compliance: A Comparative Simulation Study of the Instrumental Variables and Per-Protocol Analyses
Applications
Finds true medicine effects even if people don't follow rules.
Generalizing causal effects with noncompliance: Application to deep canvassing experiments
Methodology
Helps predict treatment effects on new groups.
Design and Analysis Considerations for Causal Inference under Two-Phase Sampling in Observational Studies
Methodology
Makes surveys more accurate with less money.