Causal Post-Processing of Predictive Models

Abstract: Decision makers across various domains rely on predictive models to guide individual-level intervention decisions. However, these models are typically trained to predict outcomes rather than causal effects, leading to misalignments when they are used for causal decision making. Experimental data to train effective causal effect models often is limited. To address this issue, we propose causal post-processing (CPP), a family of techniques for refining predictive scores to better align with causal effects using limited experimental data. Rather than training separate causal models for each intervention, causal post-processing can adapt existing predictive scores to support different decision-making requirements, such as estimating effect sizes, ranking individuals by expected effects, or classifying individuals based on an intervention threshold. We introduce three main CPP approaches -- monotonic post-processing, correction post-processing, and model-based post-processing -- each balancing statistical efficiency and flexibility differently. Through simulations and an empirical application in advertising, we demonstrate that causal post-processing improves intervention decisions, particularly in settings where experimental data is expensive or difficult to obtain at scale. Our findings highlight the advantages of integrating non-causal predictive models with experimental data, rather than treating them as competing alternatives, which provides a scalable and data-efficient approach to causal inference for decision making.