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Randomized controlled trials (RCTs) are considered as the gold standard fortesting causal hypotheses in the clinical domain. However, the investigation ofprognostic variables of patient outcome in a hypothesized cause-effect route isnot feasible using standard statistical methods. Here, we propose a newautomated causal inference method (AutoCI) built upon the invariant causalprediction (ICP) framework for the causal re-interpretation of clinical trialdata. Compared to existing methods, we show that the proposed AutoCI allows toefficiently determine the causal variables with a clear differentiation on tworeal-world RCTs of endometrial cancer patients with mature outcome andextensive clinicopathological and molecular data. This is achieved viasuppressing the causal probability of non-causal variables by a wide margin. Inablation studies, we further demonstrate that the assignment of causalprobabilities by AutoCI remain consistent in the presence of confounders. Inconclusion, these results confirm the robustness and feasibility of AutoCI forfuture applications in real-world clinical analysis.

Automated causal inference in application to randomized controlled clinical trials