Inferring Effect Ordering Without Causal Effect Estimation (2206.12532v5)

Abstract: Predictive models are often employed to guide interventions across various domains, such as advertising, customer retention, and personalized medicine. These models often do not estimate the actual effects of interventions but serve as proxies, suggesting potential effectiveness based on predicted outcomes. Our paper addresses the critical question of when and how these predictive models can be interpreted causally, specifically focusing on using the models for inferring effect ordering rather than precise effect sizes. We formalize two assumptions, full latent mediation and latent monotonicity, that are jointly sufficient for inferring effect ordering without direct causal effect estimation. We explore the utility of these assumptions in assessing the feasibility of proxies for inferring effect ordering in scenarios where there is no data on how individuals behave when intervened or no data on the primary outcome of interest. Additionally, we provide practical guidelines for practitioners to make their own assessments about proxies. Our findings reveal not only when it is possible to reasonably infer effect ordering from proxies, but also conditions under which modeling these proxies can outperform direct effect estimation. This study underscores the importance of broadening causal inference to encompass alternative causal interpretations beyond effect estimation, offering a foundation for future research to enhance decision-making processes when direct effect estimation is not feasible.