Applications in the Social Sciences

Applications in the Social Sciences

Statistics plays a central role in the social sciences, applying rigorous quantitative methods to understand and solve some of the greatest challenges affecting human societies. Statistics is especially critical at a time when the volume and type of available data are transforming the social sciences.

Berkeley statistics faculty study pressing problems from across the social sciences: economics, policy, and quantitative finance; the census, official statistics, and demography; political science and election integrity and security; sociology, social network analysis, and computational social science; and education and psychometrics. Faculty have close connections and joint appointments with other UC Berkeley departments (including Demography, Political Science, Public Policy) and are core members of the campus’s new computational social science training program. Faculty also have active collaborations with social science researchers, non-profit organizations, companies, and government agencies from across the world.

The innovative work today builds on the Berkeley statistics department’s long history at the intersection of statistics and the social sciences. This is particularly true in the use of statistics in the law—in a landmark case on census adjustment in 1990, the US Supreme Court ruled unanimously in favor of an analysis from faculty Ken Wachter and (the late) David Freedman. Berkeley Statistics faculty continue to serve as leading experts on related matters.


Peng Ding

causal inference in experiments and observational studies, with applications to biomedical and social sciences; contaminated data including missing data, measurement error, and selection bias

Noureddine El Karoui

high-dimensional statistics, random matrices, high-dimensional robust regression, high-dimensional M-estimation, the bootstrap and resampling in high-dimension, limit theorems and statistical inference, applied statistics, auction theory from the bidder standpoint,…

Avi Feller

applied statistics, theoretical statistics, Bayesian statistics, machine learning, statistics in social sciences

Jared Fisher formal headshot

Bayesian statistics, causal inference, time series, applied statistics

Jon McAuliffe

machine learning, statistical prediction, variational inference, statistical computing, optimization


causal inference, health services & policy analysis, biostatistics, discrete optimization

photo of P.B. Stark

uncertainty quantification and inference, inverse problems, nonparametrics, risk assessment, earthquake prediction, election auditing, geomagnetism, cosmology, litigation, food/nutrition


Mathematical demography, statistical models for the evolution of aging, systematic constraints and random influences that shape the structure of human populations, computer simulation methods.