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Simplifying Matching Methods for Causal Inference (University of Wisconsin at Madison), at University of Wisconsin at Madison, Department of Population Health Sciences, Monday, October 11, 2021:
Slides
We show how to use matching methods for causal inference to ameliorate model dependence -- where small, indefensible changes in model specification have large impacts on our conclusions. We introduce methods that are simpler, more powerful, and easier to understand than existing approaches. We also show that propensity score matching, an enormously popular approach, often accomplishes the opposite of its intended goal -- increasing imbalance, inefficiency, model dependence, and bias -- and should be replaced with other matching methods in applications.  See ... Read more about Simplifying Matching Methods for Causal Inference (University of Wisconsin at Madison)
How to Measure Legislative District Compactness If You Only Know it When You See it (IPSA World Congress), at IPSA World Congress, Panel on Innovative Methods in Political Science, Monday, July 12, 2021

 

 

To deter gerrymandering, many state constitutions require legislative districts to be "compact." Yet, the law offers few precise definitions other than "you know it when you see it," which effectively implies a common understanding of the concept. In contrast, academics have shown that compactness has multiple dimensions and have generated many conflicting measures. We hypothesize that both are correct -- that compactness is complex and multidimensional, but a common understanding exists across people. We develop a survey to elicit this understanding,...

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Scientific Measurement in Redistricting Research (Princeton University), at The Princeton Gerrymandering Project, Friday, May 21, 2021:
Slides
We discuss the essential requirements for the measurement of any quantity of interest as applied to redistricting research.  Most importantly, a quantity of interest must be defined separately from its measure, without which empirical estimates cannot evaluated or improved. Only with such a standard can we learn about an electoral system or understand fundamental concepts in the field such as legislative compactness, partisan bias, electoral responsiveness, among others (with or without differentially private noise applied to census data), all of which we will illustrate. 
How to Measure Legislative District Compactness If You Only Know it When You See it (Department of Biomedical Informatics, Harvard Medical School), at Department of Biomedical Informatics, Harvard Medical School, Tuesday, March 16, 2021:
Slides

To deter gerrymandering, many state constitutions require legislative districts to be "compact." Yet, the law offers few precise definitions other than "you know it when you see it," which effectively implies a common understanding of the concept. In contrast, academics have shown that compactness has multiple dimensions and have generated many conflicting measures. We hypothesize that both are correct -- that compactness is complex and multidimensional, but a common understanding exists across people. We develop a survey to elicit this understanding, with high reliability (in data where...

Read more about How to Measure Legislative District Compactness If You Only Know it When You See it (Department of Biomedical Informatics, Harvard Medical School)
Statistically Valid Inferences from Privacy Protected Data (Interagency Arctic Research Policy Committee), at Interagency Arctic Research Policy Committee, Thursday, November 19, 2020:
Slides
Unprecedented quantities of data that could help social scientists understand and ameliorate the challenges of human society are presently locked away inside companies, governments, and other organizations, in part because of worries about privacy violations. We address this problem with a general-purpose data access and analysis system with mathematical guarantees of privacy for individuals who may be represented in the data, statistical guarantees for researchers seeking insights from it, and protection for society from some fallacious scientific conclusions. We build on the standard of "... Read more about Statistically Valid Inferences from Privacy Protected Data (Interagency Arctic Research Policy Committee)
Empowering Social Science to Understand and Ameliorate Major Challenges of Human Society (Federal Interagency Conference on Social Science and Big Data), at Federal Interagency Conference on Social Science and Big Data, Wednesday, September 30, 2020:
Slides

Social scientists can understand and ameliorate some of the major challenges of human society by making new connections across academia, government, and industry; developing new methods of analyzing data, rather than merely watching big data get bigger; and ensuring they have the flexibility to ask new questions that arise in data analysis rather than sticking to the original ones posed. I make these points in discussions of research projects I've been lucky to lead across the US, China, Mexico, and Italy.  

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Statistically Valid Inferences from Privacy Protected Data (Google, Inc) Friday, September 18, 2020:
Slides

Unprecedented quantities of data that could help social scientists understand and ameliorate the challenges of human society are presently locked away inside companies, governments, and other organizations, in part because of worries about privacy violations. We address this problem with a general-purpose data access and analysis system with mathematical guarantees of privacy for individuals who may be represented in the data, statistical guarantees for researchers seeking insights from it, and protection for society from some fallacious scientific conclusions. We build on the standard...

Read more about Statistically Valid Inferences from Privacy Protected Data (Google, Inc)

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