CACE-MM: using mixed methods to strengthen causal inference in medicine and public health

Causal inference in medicine and public health almost always depends on untestable assumptions. Estimating valid causal effects thus often requires substantive knowledge about the study context that quantitative methods alone cannot provide. This paper introduces CACE-MM, a mixed methods framework that integrates qualitative approaches with complier average causal effect (CACE) estimation to strengthen causal decision-making and assess the plausibility of key underlying assumptions. CACE-MM is the first framework to systematically integrate qualitative inquiry with causal effect estimation in the presence of noncompliance. We present a proof-of-concept application of CACE-MM using data from The Youth Empowerment Study (YES), a randomized trial of a trauma-informed intervention for youth involved in the juvenile legal system ( n = 630). Following CACE analyses using an instrumental variable approach (invoking the exclusion restriction) and principal score approach (invoking the assumption of principal ignorability), we conducted 10 semi-structured interviews with key informants. Qualitative data were analyzed using inductive open coding followed by deductive mapping to the Capability, Opportunity, Motivation-Behavior (COM-B) model and the Theoretical Domains Framework (TDF). The study team assessed the plausibility of the key assumptions underlying each quantitative approach before and after the qualitative inquiry to generate integrated metainference about assumptions. Qualitative findings identified predictors of participation and outcomes that were not captured in baseline quantitative measures, raising concerns about the plausibility of principal ignorability. Interviews also clarified how meaningful exposure to intervention components was understood by implementers, informing the defensibility of participation thresholds used to invoke the exclusion restriction. More broadly, the findings demonstrate how qualitative inquiry can inform key analytic decisions that shape causal estimates, including how participation is defined, which covariates should be prioritized for measurement, and whether particular identification strategies are appropriate for specific outcomes. Building on these insights, we propose the full CACE-MM framework, incorporating both exploratory and explanatory phases, and outline decision points to guide application in applied health research. CACE-MM offers a rigorous and systematic approach for integrating qualitative evidence into causal analyses and ultimately strengthening the transparency and interpretability of the CACE in applied health research. ClinicalTrials.gov NCT03242447.