Presentations

Statistically Valid Inferences from Privacy Protected Data (Interagency Arctic Research Policy Committee), at Interagency Arctic Research Policy Committee, Thursday, November 19, 2020:
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:

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:

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)
Statistically Valid Inferences from Privacy Protected Data (Harvard, Privacy Tools Project), at Harvard University (via Zoom), Monday, April 20, 2020:

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 (Harvard, Privacy Tools Project)
Statistically Valid Inferences from Privacy Protected Data (Google) Friday, March 20, 2020:

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)
Statistically Valid Inferences from Privacy Protected Data (Webcast, Project TIER), at Webcast, Project TIER (Teaching Integrity in Empirical Research), Friday, February 14, 2020:

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 (Webcast, Project TIER)
Statistically Valid Inferences from Privacy Protected Data (Harvard University, Applied Statistics Workshop) Wednesday, February 5, 2020:

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 (Harvard University, Applied Statistics Workshop)
Simplifying Matching Methods for Causal Inference (Hebrew University of Jerusalem) Wednesday, January 1, 2020:
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 (Hebrew University of Jerusalem)

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