Causal Post-Processing of Predictive Models

TL;DR

This paper introduces causal post-processing (CPP), a set of techniques that leverage limited experimental data to refine predictive models, improving their alignment with causal decision-making in various applications.

Contribution

It proposes a unified framework for causal post-processing that enhances predictive models using experimental data, bridging the gap between prediction and causal impact estimation.

Findings

CPP improves intervention decision quality in digital advertising.

The methods effectively utilize limited experimental data.

Enhanced models better capture causal signals for resource allocation.

Abstract

Organizations increasingly rely on predictive models to decide who should be targeted for interventions, such as marketing campaigns, customer retention offers, or medical treatments. Yet these models are usually built to predict outcomes (e.g., likelihood of purchase or churn), not the actual impact of an intervention. As a result, the scores (predicted values) they produce are often imperfect guides for allocating resources. Causal effects can be estimated with randomized experiments, but experiments are costly, limited in scale, and tied to specific actions. We propose causal post-processing (CPP), a family of techniques that uses limited experimental data to refine the outputs of predictive models, so they better align with causal decision making. The CPP family spans approaches that trade off flexibility against data efficiency, unifying existing methods and motivating new ones. Through simulations and an empirical study in digital advertising, we show that CPP can improve intervention decisions, particularly when predictive models capture a useful but imperfect causal signal. Our results show how organizations can combine predictive modeling with experimental evidence to make more effective and scalable intervention decisions.