BIOS Seminar: Nonparametric Casual Effects

Edward Kennedy, PhD, assistant professor in the Department of Statistics and Data Science at Carnegie Mellon University, will present this week’s biostatistics seminar titled, “Nonparametric Causal Effects Based on Incremental Propensity Score Interventions.”

Dr. Kennedy joined Carnegie Mellon as Assistant Professor in 2016. His research interests include causal inference, missing data, functional estimation, machine learning, and general nonparametrics, especially in settings involving high dimensional and otherwise complex data. He is particularly interested in applications in criminal justice, health services, medicine and public policy.

Abstract:

Most work in causal inference considers deterministic interventions that set each unit’s treatment to some fixed value. However, under positivity violations these interventions can lead to nonidentification, inefficiency, and effects with little practical relevance. Further, corresponding effects in longitudinal studies are highly sensitive to the curse of dimensionality, resulting in widespread use of unrealistic parametric models. We propose a novel solution to these problems: incremental interventions that shift propensity score values rather than set treatments to fixed values. Incremental interventions have several crucial advantages. First, they avoid positivity assumptions entirely. Second, they require no parametric assumptions and yet still admit a simple characterization of longitudinal effects, independent of the number of timepoints. For example, they allow longitudinal effects to be visualized with a single curve instead of lists of coefficients. After characterizing incremental interventions and giving identifying conditions for corresponding effects, we also develop general efficiency theory, propose efficient nonparametric estimators that can attain fast convergence rates even when incorporating flexible machine learning,
and propose a bootstrap-based confidence band and simultaneous test of no treatment effect. Finally, we explore finite-sample performance via simulation, and apply the methods to study time-varying sociological effects of incarceration on entry into marriage.