Causal Predict-Then-Optimize: Treatment Effect Estimation under Downstream Optimization

Many operational decisions depend on estimating heterogeneous treatment effects and then using those estimates in constrained downstream optimization. In this setting, accurate causal prediction does not necessarily lead to good decisions, because estimation errors matter most when they occur near decision boundaries and alter the optimizer. We develop causal predict-then-optimize (CPO), which combines doubly robust causal estimation with decision-focused learning, and propose decision-calibrated causal predict-then-optimize (DC-CPO), which explicitly accounts for downstream decision sensitivity. Our framework characterizes causal uncertainty through its orientation relative to optimization boundaries and introduces a boundary-sensitive regularizer that emphasizes prediction errors along decision-relevant directions. The resulting method is designed to improve decisions rather than treatment-effect prediction accuracy alone. Across synthetic and real-world covariate designs, CPO+ achieves lower downstream regret than prediction-oriented causal learners, while DC-CPO further reduces regret and decision-boundary crossing. Our results show that causal prediction accuracy and downstream decision quality are distinct objectives, and that explicitly calibrating causal estimators to optimization boundaries can lead to better constrained treatment allocation.