Jennifer Hill

Carlos Fernández-Loría, Yanfang Hou, Foster Provost et al.

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.

Vincent Dorie, Jennifer Hill, Uri Shalit et al.

Statisticians have made great progress in creating methods that reduce our reliance on parametric assumptions. However this explosion in research has resulted in a breadth of inferential strategies that both create opportunities for more reliable inference as well as complicate the choices that an applied researcher has to make and defend. Relatedly, researchers advocating for new methods typically compare their method to at best 2 or 3 other causal inference strategies and test using simulations that may or may not be designed to equally tease out flaws in all the competing methods. The causal inference data analysis challenge, "Is Your SATT Where It's At?", launched as part of the 2016 Atlantic Causal Inference Conference, sought to make progress with respect to both of these issues. The researchers creating the data testing grounds were distinct from the researchers submitting methods whose efficacy would be evaluated. Results from 30 competitors across the two versions of the competition (black box algorithms and do-it-yourself analyses) are presented along with post-hoc analyses that reveal information about the characteristics of causal inference strategies and settings that affect performance. The most consistent conclusion was that methods that flexibly model the response surface perform better overall than methods that fail to do so. Finally new methods are proposed that combine features of several of the top-performing submitted methods.