Learning Subgroup-level Causal Actions with Maximum Interventional Probabilities

Data-driven decision support asks who should receive which intervention to maximize a predefined outcome, a question with wide applications in critical areas such as healthcare, public policy, and economics. While existing methods mostly focus on a single actionable variable, real-world applications often involve many, possibly dependent, actionable variables, of which decision makers want to intervene on only a few while the others respond. We study the problem of subgroup-level causal action design, which aims to decide which variables to intervene on, what values to set, and which subgroup of the population to apply the intervention to, so as to maximize the interventional success probability. Such partial interventions are hard to evaluate, since the response of the untouched variables depends on the causal graph among them, which observational data do not identify. We show that the graph is nevertheless not needed to find the best subgroup-level action: under a partition-based model of the outcome, the problem reduces to a standard supervised learning problem, and we give a finite-sample guarantee that controls the regret of the learned action by the prediction error of the learned model. We instantiate the approach with a decision tree learner, the Subgroup-level Causal Action Tree (SCAT), and, to validate its empirical performance, construct a semi-synthetic benchmark with multiple treatments based on ACIC 2016. Compared to policy learning methods adapted to our setting, SCAT finds the subgroup-level action with the highest interventional success probability on most datasets, estimates that probability most accurately, and is substantially faster.