Automated causal inference in application to randomized controlled clinical trials
This addresses the challenge of causal inference in clinical trials for medical researchers, though it appears incremental as it builds upon the existing invariant causal prediction framework.
The authors tackled the problem of identifying prognostic variables in randomized controlled trials (RCTs) where standard statistical methods fail, proposing AutoCI, an automated causal inference method that efficiently determines causal variables with clear differentiation on two real-world endometrial cancer RCTs.
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.