  Randomized controlled trials (RCTs) are considered as the gold standard for
testing causal hypotheses in the clinical domain. However, the investigation of
prognostic variables of patient outcome in a hypothesized cause-effect route is
not feasible using standard statistical methods. Here, we propose a new
automated causal inference method (AutoCI) built upon the invariant causal
prediction (ICP) framework for the causal re-interpretation of clinical trial
data. Compared to existing methods, we show that the proposed AutoCI allows to
efficiently determine the causal variables with a clear differentiation on two
real-world RCTs of endometrial cancer patients with mature outcome and
extensive clinicopathological and molecular data. This is achieved via
suppressing the causal probability of non-causal variables by a wide margin. In
ablation studies, we further demonstrate that the assignment of causal
probabilities by AutoCI remain consistent in the presence of confounders. In
conclusion, these results confirm the robustness and feasibility of AutoCI for
future applications in real-world clinical analysis.
