Presentations

How the news media activate public expression and influence national agendas, at St. Louis Area Methods Meeting (SLAMM), Iowa State University, Friday, April 20, 2018:
This talk reports on the results of first large scale randomized news media experiment. We demonstrate that even small news media outlets can cause large numbers of Americans to take public stands on specific issues, join national policy conversations, and express themselves publicly—all key components of democratic politics—more often than they would otherwise. After recruiting 48 mostly small media outlets, and working with them over 5 years, we chose groups of these outlets to write and publish articles on subjects we approved, on dates we randomly assigned. We estimate the causal effect... Read more about How the news media activate public expression and influence national agendas
How the news media activate public expression and influence national agendas Friday, February 16, 2018:
This talk reports on the results of first large scale randomized news media experiment. We demonstrate that even small news media outlets can cause large numbers of Americans to take public stands on specific issues, join national policy conversations, and express themselves publicly—all key components of democratic politics—more often than they would otherwise. After recruiting 48 mostly small media outlets, and working with them over 5 years, we chose groups of these outlets to write and publish articles on subjects we approved, on dates we randomly assigned. We estimate the causal effect... Read more about How the news media activate public expression and influence national agendas
How to Measure Legislative District Compactness If You Only Know it When You See it, at Stony Brook University, Institute for Advanced Computational Science, Thursday, February 15, 2018:
To prevent gerrymandering and to encourage a form of democratic representation, many state constitutions and judicial opinions require US legislative districts be "compact." Yet, few precise definitions are offered other than "you know it when you see it," effectively assuming the existence of a common understanding of the concept. In contrast, academics have concluded that the concept has multiple theoretical dimensions requiring large numbers of conflicting empirical measures. This has proved extremely challenging for courts tasked with adjudicating compactness. We hypothesize that both are... Read more about How to Measure Legislative District Compactness If You Only Know it When You See it
Matching Methods for Causal Inference, at Microsoft, Cambridge, Friday, January 19, 2018:
This presentation shows how to use matching in causal inference to ameliorate model dependence -- where small, indefensible changes in model specification have large impacts on our conclusions. We introduce matching methods that are simpler, more powerful, and easier to understand. We also show that the most commonly used existing method, propensity score matching, should rarely be used. Easy-to-use software is available to implement all methods discussed.
How the news media activate public expression and influence national agendas, at University of Toronto, Friday, January 12, 2018:

This talk reports on the results of first large scale randomized news media experiment. We demonstrate that even small news media outlets can cause large numbers of Americans to take public stands on specific issues, join national policy conversations, and express themselves publicly—all key components of democratic politics—more often than they would otherwise. After recruiting 48 mostly small media outlets, and working with them over 5 years, we chose groups of these outlets to write and publish articles on subjects we approved, on dates we randomly assigned. We estimate the...

Read more about How the news media activate public expression and influence national agendas
Simplifying Matching Methods for Causal Inference, at Harvard University, Department of Biostatistics, HIV Working Group, 11/17/2017, Friday, November 17, 2017:
This presentation shows how to use matching to ameliorate model dependence -- where small, indefensible changes in model specification have large impacts on our conclusions. We introduce matching methods that are simpler, more powerful, and easier to understand. We also show that the most commonly used existing method, propensity score matching, should rarely be used. Easy-to-use software is available to implement all methods discussed.
Simplifying Matching Methods for Causal Inference, at Harvard Health Policy and Insurance Research Seminar, Monday, October 16, 2017:
This presentation shows how to use matching to ameliorate model dependence -- where small, indefensible changes in model specification have large impacts on our conclusions. We introduce matching methods that are simpler, more powerful, and easier to understand. We also show that the most commonly used existing method, propensity score matching, should rarely be used. Easy-to-use software is available to implement all methods discussed.
Reverse Engineering Chinese Government Information Controls, at Paul and Marica Wythes Center on Contemporary China, Princeton University, Wednesday, October 11, 2017:
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 (in Science here, and the American Poltiical Science Review ... Read more about Reverse Engineering Chinese Government Information Controls
How the Chinese Government Fabricates Social Media Posts for Strategic Distraction, not Engaged Argument, at Harvard University and National Taiwan University, Friday, September 29, 2017:
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 ... Read more about How the Chinese Government Fabricates Social Media Posts for Strategic Distraction, not Engaged Argument

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