People

PhD, Statistics, Harvard University
MS, Statistics, Rutgers University
BA, Economics, Swarthmore College

Jennifer Hill develops and evaluates methods to help answer the types of causal questions that are vital to policy research and scientific development. In particular she focuses on situations in which it is difficult or impossible to perform traditional randomized experiments, or when even seemingly pristine study designs are complicated by missing data or hierarchically structured data. Most recently Dr. Hill has been pursuing two intersecting strands of research. The first focuses on Bayesian nonparametric methods that allow for flexible estimation of causal models and are less time-consuming and more precise than competing methods (e.g. propensity score approaches). The second line of work pursues strategies for exploring the impact of violations of typical causal inference assumptions such as ignorability (all confounders measured) and common support (overlap). Dr. Hill has published in a variety of leading journals including the Journal of the American Statistical Association, Statistical Science, American Political Science Review, American Journal of Public Health, and Developmental Psychology.

Recent

SDS 607: Inferring Causality

SuperDataScience

Jennifer Hill was a guest on the podcast discussing the role of causality in data science applications.