Hierarchical Bias-Driven Stratification for Interpretable Causal Effect
  Estimation
Lucile Ter-Minassian Liran Szlak Ehud Karavani Chris Holmes Yishai Shimoni
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.