Hierarchical Bias-Driven Stratification for Interpretable Causal Effect Estimation
This work addresses the need for transparent causal models in policy decision-making, though it appears incremental as it builds on decision trees with custom objectives.
The paper tackles the problem of interpretable causal effect estimation from observational data by introducing BICauseTree, a method that identifies clusters for local natural experiments and reduces treatment allocation bias, showing comparable performance to existing approaches.
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 that identifies clusters where natural experiments occur locally. Our approach builds on decision trees with a customized objective function to improve balancing and reduce treatment allocation bias. Consequently, it can additionally detect subgroups presenting positivity violations, exclude them, and provide a covariate-based definition of the target population we can infer from and generalize to. We evaluate the method's performance using synthetic and realistic datasets, explore its bias-interpretability tradeoff, and show that it is comparable with existing approaches.