Demystifying inconsistent two-sample mendelian randomization estimations using selection diagram

Two-Sample Mendelian Randomization (TSMR) analysis is a widely used method for inferring causal effect in the presence of unmeasured confounding. However, causal inferences may be biased if the distributions of key variables (e.g., exposures, outcomes, and confounders) differ across populations. Such discrepancies in the distributions of key variables between the two populations are referred to as different local mechanisms. This paper aims to clarify the impact of different local mechanisms on the estimation of the Local Average Treatment Effect (LATE) in TSMR analyses using selection diagrams. We first uncover and formally define the Complete and Partial Inconsistent TSMR Estimations (InTSMRE). Subsequently, we propose a criterion of No InTSMRE in the context of continuous and binary outcomes. Following this, we introduce the LATE Ratio to evaluate the deviation of the LATE estimate from the true causal effect. Finally, we demonstrate that the violation of the Monotonicity condition exacerbates the occurrences of the Complete InTSMRE; otherwise only the Partial InTSMRE occurs. Additionally, through simulation studies, we illustrate the specific conditions under which these InTSMRE arise. We explore the LATEs of Waist-to-hip ratio on Type 2 diabetes in European and mixed populations, demonstrating the phenomenon of the InTSMRE.