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Jennifer Hill

Professor of Applied Statistics

Applied Statistics, Social Science, and Humanities

212-992-7677

Jennifer Hill develops and evaluates methods that help us answer the 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 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). Hill has published in a variety of leading journals including Journal of the American Statistical Association, Statistical Science, 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 Practice and Research at the Intersection of Information, Society, and Methodology (PRIISM) and Co-Director of and the Master's of Science Program in Applied Statistics for Social Science Research (A3SR). The A3SR program has a new concentration in Data Science for Social Impact! As far as we know this is the first degree granting program in Statistics or Data Science for Social Impact or Social Good in the world!

Selected Publications

Courses

Causal Inference

Course provides students with a basic knowledge of both how to perform analyses and critique the use of some more advanced statistical methods useful in answering policy questions. While randomized experiments will be discussed, the primary focus will be the challenge of answering causal questions using data that do not meet such standards. Several approaches for observational data including propensity score methods, instrumental variables, difference in differences, fixed effects models and regression discontinuity designs will be discussed. Examples from real public policy studies will be used to illustrate key ideas and methods.
Course #
APSTA-GE 2012
Units
3
Term
Fall
Faculty

Professors

Jennifer Hill ,
Department

Data Science for Social Impact

Course focuses on the competencies required and the issues that arise Course focuses on how analysts use data and quantitative evidence to impact policy and practice.Students will learn how to gather and analyze data to address questions about program efficacy and efficient targeting of resources. Topics will include how to choose organizational partners, implement change, build trust with organizations and civic agencies, satisfy the needs of stakeholders and manage legal, ethical, and logistical constraints.Students will discuss real case studies and appropriate ways to address them.
Course #
APSTA-GE 2331
Units
3
Term
Fall
Faculty

Professors

Jennifer Hill ,
Department

Missing Data

Course provides students with a basic knowledge of missing data analysis, beginning with the types of missing data mechanisms (e.g., missing completely at random). We then discuss the problems with ignoring missing data and examine problems with conventional fixes. Single imputation with noise is contrasted with multiple imputation approaches.Real examples from policy research are given throughout. More advanced topics include pattern mixture models and handling data that are not missing at random
Course #
APSTA-GE 2013
Units
2
Term
Spring
Faculty

Professors

Jennifer Hill ,
Department

Statistical Consulting Research Seminar

This course is designed to assist graduate students in the quantitative methods specific to the design and analysis of their theses. In this seminar format, under the guidance of one or more statistical faculty members, students will have opportunity to present and defend their scholarly work-in-progress. They will also be required to critique and provide constructive suggestions for their fellow students. The focus of critiques will be on the research methodology and other statistical issues. Students will additionally benefit from being able to observe how the participating faculty diagnosis and solve statistical issues that arise in others' presented work and to benefit from this advice in their own work. In essence this course provides training in statistical consulting along with detailed feedback on one's dissertation research
Course #
APSTA-GE 2401
Units
1 - 3
Term
Fall
Faculty

Professors

Jennifer Hill ,
Department

Statistical Mysteries and How to Solve Them

An introductory quantitative & statistical reasoning course designed to help students acquire statistical literacy & competency to survive in a data-rich world. The course introduces students to basic concepts in probability, research design, descriptive statistics, & simple predictive models to help them to become more savvy consumers of the information they will routinely be exposed to in their personal, academic & professional lives. Course material will be conveyed through video clips, case studies, puzzle solving, predictive competitions, & group discussions. Liberal Arts Core/CORE Equivalent - satisfies the requirement for Quantitative Reasoning
Course #
APSTA-UE 10
Units
4
Term
Spring
Faculty

Professors

Jennifer Hill , Daphna Harel ,
Department
Liberal Arts Core