Learning Cost-Effective and Interpretable Treatment Regimes

Decision makers, such as doctors and judges, make crucial decisions such as recommending treatments to patients, and granting bail to defendants on a daily basis. Such decisions typically involve weighing the potential benefits of taking an action against the costs involved. In this work, we aim to automate this task of learning cost-effective, interpretable and actionable treatment regimes. We formulate this as a problem of learning a decision list – a sequence of if-then-else rules – that maps characteristics of subjects (eg., diagnostic test results of patients) to treatments. This yields an end-to-end individualized policy for tests and treatments. We propose a novel objective to construct a decision list which maximizes outcomes for the population, and minimizes overall costs. Since we do not observe the outcomes corresponding to counterfactual scenarios, we use techniques from causal inference literature to infer them. We model the problem of learning the decision list as a Markov Decision Process (MDP) and employ a variant of the Upper Confidence Bound for Trees (UCT) strategy which leverages customized checks for pruning the search space effectively. Experimental results on real world observational data capturing judicial bail decisions and treatment recommendations for asthma patients demonstrate the effectiveness of our approach.