Affiliation: New York University – Steinhardt

Educational Background: BSc, Swarthmore College; master’s, Statistics, Rutgers; PhD, Statistics, Harvard University

Jennifer Hill always liked math, but her interests turned toward social science in her undergraduate years and she became an economics major. After college, she worked in the corporate world for a few years before deciding to go back to school to study statistics. She completed a master’s program at Rutgers to see if it was a good fit, and it was, so she then committed to a PhD program.

During that time, Hill worked with Rob Hollister—a labor economist at Swarthmore College—on a paper about evaluating community-based initiatives. It piqued her interest in social policy and causal inference, so she looked for doctoral programs in which faculty were focusing on either causal inference or social science–related questions. In graduate school, she worked on projects in which she could think about policy issues or causal inference methods and looked for job placements in which she could use those skills when she graduated.

Hill completed a postdoctoral fellowship in social policy at a school of social work, and her first job was as an assistant professor at a policy school. Now she works at NYU Steinhardt, a school with a mix of disciplines that values interdisciplinary work and can be broadly thought of as an institution focused on promoting social good. The group she is part of works on methods to achieve those aims. 

Hill divides her time between activities and methodological research related to causal inference and machine learning. In that arena, she’s most excited about creating software that makes it easier for applied researchers to use sophisticated methods in a way that provides guardrails but also a deeper understanding of the methods they are using and why. The tool she built is called thinkCausal. 

Hill also teaches, and it’s the part of her job she both loves and dreads the most. Every year, she becomes more humble about her ability to effectively impart learning, but she keeps trying. She enjoys the applied research she is able to contribute to, which ranges from demography to criminology, education, and health. For Hill, answering real questions with real data is the best and biggest challenge there is.