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Big Data is Not About the Data!, at Abt Associates, Thursday, September 28, 2017:
Slides
The spectacular progress the media describes as "big data" has little to do with the growth of 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 ... Read more about Big Data is Not About the Data!
Fabricating News In Chinese Social Media, at Congress of the Mexican Political Science Association, University of Quintana Roo, Cancun, Mexico, Friday, September 15, 2017:
Slides

This talk is based on this paper (in the current issue of 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 American Poltiical Science Review ...

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Big Data Reveals Made Up Data: How the Chinese Government Fabricates Social Media Posts for Strategic Distraction, not Engaged Argument, at University of Texas at Austin, Thursday, September 7, 2017
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).... Read more about Big Data Reveals Made Up Data: How the Chinese Government Fabricates Social Media Posts for Strategic Distraction, not Engaged Argument
Detecting and Reducing Model Dependence in Causal Inference, at Peking University, Wednesday, July 26, 2017:
Slides

This presentation discusses methods of detecting counterfactuals (predictions, what if questions, and casual inferences) far enough from the data that any inferences based on it will yield highly model dependent inferences -- where small, indefensible changes in a model specification have large impacts on our conclusions. The talk also shows how to ameliorate many situations like this via  matching for causal inference. We introduce matching methods that are simpler, more powerful, and easier to understand. We also show that the most commonly used...

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How to Measure Legislative District Compactness If You Only Know it When You See it, at Society for Political Methodology Conference, University of Wisconsin, Friday, July 14, 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...

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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...

Read more about Big Data is Not About the Data!

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