Principal Stratification for Quantile Causal Effects Under Partial Compliance

• Activity Number: 487 - Novel Causal Inference Methods for Epidemiology Studies
• Type: Contributed
• Date/Time: Wednesday, August 10, 2022 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Shuo Sun* and Johanna G. Nešlehová and Erica EM Moodie
• Companies: McGill University and McGill University and McGill University
• Keywords: causal inference; principal strata; copula model; quantile regression; COVID-19

Abstract

Within the principal stratification framework in causal inference, the majority of the literature has focused on binary compliance and modelling means. Yet in some research areas, compliance is partial, and research questions are concerned with causal effects on (possibly high) quantiles. Modelling partial compliance is challenging because it can suffer from lack of identifiability. We develop an approach to estimate quantile causal effects within a principal stratification framework, where principal strata are defined by the bivariate vector of partial compliance to the two levels of a binary intervention. We propose a conditional copula approach to impute the missing potential compliance and estimate the principal quantile treatment effect surface at high quantiles, allowing the copula association parameter to vary with the covariates. Moreover, we describe precise assumptions on which the proposed approach is based, and investigate the finite sample behavior of our method by a simulation study. The proposed approach is used to study the 90th principal quantile treatment effect of executive stay-at-home orders on mitigating the risk of COVID-19 transmission in the United States.