Tech Science Seminar

Since most social science research relies upon multiple data sources, merging data sets is an essential part of researchers' workflow. Unfortunately, a unique identifier that unambiguously links records is often unavailable, and data may contain missing and inaccurate information. These problems are severe especially when merging large-scale administrative records. We develop a fast and scalable algorithm to implement a canonical probabilistic model of record linkage that has many advantages over deterministic methods frequently used by social scientists. The proposed methodology efficiently handles millions of observations while accounting for missing data and measurement error, incorporating auxiliary information, and adjusting for uncertainty about merging in post-merge analyses. We conduct comprehensive simulation studies to evaluate the performance of our algorithm in realistic scenarios. We also apply our methodology to merging campaign contribution records, survey data, and nationwide voter files. An open-source software is available for implementing the proposed methodology. Our method is used to validate the self-reported turnout in the 2016 American National Election Study. Our turnout validation data are available at the ANES website. See this paper for details. Speaker: Kosuke Imai is Professor in the Department of Government and the Department of Statistics at Harvard University. He is also an affiliate of the Institute for Quantitative Social Science. Before moving to Harvard in 2018, Imai taught at Princeton University for 15 years where he was the founding director of the Program in Statistics and Machine Learning. Outside of Harvard, Imai is currently serving as the President of the Society for Political Methodology and was elected fellow in 2017. He is also Professor of Visiting Status in the Faculty of Law and Graduate Schools of Law and Politics at the University of Tokyo. After obtaining a B.A. in Liberal Arts from the University of Tokyo (1998), Imai received an A.M. in Statistics (2002) and a Ph.D. in political science (2003) from Harvard University. Imai's research area is political methodology and more generally applied statistics in the social sciences. He has extensively worked on the development and applications of statistical methods for causal inference with experimental and observational data. Other areas of his methodological research are survey methodology and computational algorithms for data-intensive social science research. His substantive applications range from the randomized evaluation of Mexican universal health insurance program to the study of public opinion and insurgent violence in Afghanistan. Weekly seminars on Technology Science from expert researchers, public interest groups, and others on the social impact of technology and its unforeseen consequences. For more details, see the Tech Science schedule.