Causalteshap: discerning predictive from prognostic features for treatment effect analysis

Abstract

Treatment effect analysis investigates the effect of a treatment or intervention. The variables that will determine the treatment effect are called, predictive variables, while prognostic variables determine the outcome regardless of treatment, based on existing conditions on characteristics. The identification of these predictive factors facilitates understanding the treatment effect and even allows for improving its success. However, in many cases, the predictive factors of a treatment or intervention are unknown. Furthermore, methods to find these predictive factors are limited and only focus on quantifying the predictive performance of a CATE estimator instead of discerning predictive from prognostic variables. Therefore, to find these predictive variables we present Causalteshap. Causalteshap is a Shapley-based method that leverages multiple statistical tests and treatment effect estimators to discern prognostic from predictive features. The method is benchmarked on multiple fully synthetic datasets and four semi-synthetic datasets. In most of these benchmarks, Causalteshap demonstrates high precision and recall performances above 0.9. Subsequently, Causalteshap is applied to a real-world ICU use case using the AmsterdamUMCdb dataset. We analyzed the effect of Noradrenaline on Atrial Fibrillation in the ICU to display the potential of Causalteshap as a tool for treatment effect analysis. Our results demonstrate that Causalteshap has the potential of combining treatment effect estimators with Shapley values and statistical tests to provide a novel method for discerning predictive from prognostic features in treatment effect analysis and making understanding treatment effects more accessible.