Hierarchical Bias-Driven Stratification for Interpretable Causal Effect Estimation
用于可解释因果效应估计的分层偏差驱动分层方法
Interpretability and transparency are essential for incorporating causal effect models from observational data into policy decision-making. They can provide trust for the model in the absence of ground truth labels to evaluate the accuracy of such models. To date, attempts at transparent causal effect estimation consist of applying post hoc explanation methods to black-box models, which are not interpretable. Here, we present BICauseTree: an interpretable balancing method…