Randomized controlled trials with similar protocols may yield conflicting findings when the distribution of relevant effect modifiers differs across study populations. Yet no formal statistical framework exists for defining and assessing whether conflicting trials are reconcilable, despite the importance of this question for evidence synthesis and regulatory decision making. To address this gap, we develop a causal inference framework for evaluating conditional and marginal reconcilability on additive and multiplicative scales in the presence of unmeasured effect modifiers. Within this framework, we use proxy variables for hypothesized unmeasured effect modifiers to develop regression-based tests of conditional reconcilability under parametric structural models. To assess marginal reconcilability, we extend existing transportability methods and develop an equivalence testing framework. We also introduce a reconciliation proportion to quantify the degree of marginal reconciliation. We illustrate these methods using the conflicting Meis and PROLONG trials of 17-alpha-hydroxyprogesterone caproate for preventing recurrent preterm birth. The analyses provided limited evidence that unmeasured effect modifiers such as cervical length, as captured by the selected proxies, were sufficient to marginally reconcile the trials. These findings demonstrate how proximal reconciliation methods may help regulators, researchers, and clinicians evaluate whether differences in study populations explain conflicting trial findings.

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