Presentations

How to Measure Legislative District Compactness If You Only Know it When You See it, at Hubert M. Blalock Memorial Lecture, University of Michigan, Wednesday, July 12, 2017:

Slides

The US Supreme Court, many state constitutions, and numerous judicial opinions require that legislative districts be "compact," a concept assumed so simple that the only definition given in the law is "you know it when you see it." Academics, in contrast, have concluded that the concept is so complex that it has multiple theoretical dimensions requiring large numbers of conflicting empirical measures. We hypothesize that both are correct -- that the concept is complex and multidimensional, but one particular unidimensional ordering represents a common... Read more about How to Measure Legislative District Compactness If You Only Know it When You See it

Matching Methods for Causal Inference and 21 Other Topics, at Summer Institute in Computational Social Science, Princeton University, Tuesday, June 20, 2017:

Slides

This presentation discusses methods of matching for causal inference that are simpler, more powerful, and easier to understand. It shows that the most commonly used existing method, propensity score matching, should almost never be used. Easy-to-use software is available to implement all methods discussed. The presentation is followed by a class discussion about several of 21 possible research subjects. For more information, see GaryKing.org

Simplifying Matching Methods for Causal Inference, at Abt Associates, Cambridge MA, Thursday, June 1, 2017:

Slides

In this talk, Gary King introduces methods of matching for causal inference that are simpler, more powerful, and easier to understand than prior approaches. He also shows that the most commonly used existing method, propensity score matching, should almost never be used. Easy-to-use software is available to implement all methods discussed. Copies of his papers and software are available at his web site, GaryKing.org

Big Data is Not About the Data!, at Indiana University, Thursday, March 23, 2017:

Slides

The spectacular progress the media describes as "big data" has little to do with the data. Data, after all, is becoming commoditized, less expensive, and an automatic byproduct of other changes in organizations and society. More data alone doesn't generate insights; it often merely makes data analysis harder. The real revolution isn't about the data, it is about the stunning progress in the statistical and other methods of extracting insights from the data. I illustrate these points with a wide range of examples from research I've participated in, including forecasting the...

How the Chinese Government Fabricates Social Media Posts for Strategic Distraction, not Engaged Argument, at MIT Distinguished Lecture Series, IDSS, Tuesday, March 7, 2017:

Slides

This talk is based on this paper (forthcoming in the American Political Science Review), by Jen Pan, Molly Roberts, and me, along with a brief summary of our previous work (2014 in Science here, and 2013 in the APSR here)....

How the Chinese Government Fabricates Social Media Posts for Strategic Distraction, not Engaged Argument, at Duke University, Machine Learning Seminar, Wednesday, March 1, 2017:

Slides

How the Chinese Government Fabricates Social Media Posts for Strategic Distraction, not Engaged Argument, at Washington University, St. Louis, Monday, February 13, 2017:

Slides

Simplifying Matching Methods for Causal Inference, at Harvard School of Public Health, Kresge Room G2, Thursday, January 26, 2017:

Slides

This talk introduces methods of matching for causal inference that are simpler, more powerful, and easier to understand than prior approaches. Software is available to implement everything discussed. Copies of my articles on the subject are available at my website.

How the Chinese Government Fabricates Social Media Posts for Strategic Distraction, not Engaged Argument, at University of Wisconsin-Madison, Monday, January 23, 2017:

Slides

This talk is based on this paper (forthcoming in the American Political Science Review), by me, Jennifer Pan, and Margaret Roberts, along with a brief summary of our previous work. Here's an abstract: The Chinese government has long been suspected of hiring as many as 2,000,000 people to surreptitiously insert huge numbers of pseudonymous and other deceptive writings into the stream of real social media posts, as if they were the genuine opinions of ordinary people. Many academics, and most journalists...

An Improved Method of Automated Nonparametric Content Analysis for Social Science, at Texas A&M Inaugural STATA Lecture, Thursday, January 19, 2017:

Slides

A vast literature in computer science and statistics develops methods to automatically classify textual documents into chosen categories. In contrast, social scientists are often more interested in aggregate generalizations about populations of documents --- such as the percent of social media posts that speak favorably of a candidate's foreign policy. Unfortunately, trying to maximize the percent of individual documents correctly classified often yields biased estimates of statistical aggregates. Fortunately, classification is neither a necessary nor even a desirable step in estimating...

Presentations

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