Causal inference at the intersection of machine learning and statistics: opportunities and challenges
Professor Jennifer Hill, NYU Steinhardt
Jennifer Hill works on development of methods that help us to answer the causal question that are so 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 Hill has been pursuing two major
strands of research. The first focuses on Bayesian nonparametric methods that allow for flexible estimation of
causal models without the need for methods such as propensity score matching. The second line of work pursues
strategies for exploring the impact of violations of typical assumptions in this work that require that all
confounders have been measured. Hill has published in a variety of leading journals including Journal of the
American Statistical Association, American Political Science Review, American Journal of Public Health, and
Developmental Psychology. Hill earned her PhD in Statistics at Harvard University in 2000 and completed a
post-doctoral fellowship in Child and Family Policy at Columbia University's School of Social Work in 2002.
Hill is also the Co-Director of the Center for Research Involving Innovative Statistical Methodology (PRIISM)
and Co-Director of and the Master of Science Program in Applied Statistics for Social Science Research (A3SR).