Presentations

Reverse-Engineering Censorship in China, at University of Essex, Regius Lecture, Tuesday, January 12, 2016:

Chinese government censorship of social media constitutes the largest selective suppression of human communication in recorded history. In three ways, we show, paradoxically, that this large system also leaves large footprints that reveal a great deal about itself and the intentions of the government. First is an observational study where we download all social media posts before the Chinese government can read and censor those they deem objectionable, and then detect from a network of computers all over the world which are censored. Second, we conduct

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Why Propensity Scores Should Not Be Used For Matching, at Bocconi University, Milan Italy, Thursday, December 3, 2015:

This talk summarizes a paper -- Gary King and Richard Nielsen. 2015. “Why Propensity Scores Should Not Be Used for Matching” -- with this abstract:  Researchers use propensity score matching (PSM) as a data preprocessing step to selectively prune units prior to applying a model to estimate a causal effect. The goal of PSM is to reduce imbalance in the chosen pre-treatment covariates between the treated and control groups, thereby reducing the

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Why Propensity Scores Should Not Be Used For Matching, at Harvard University, Department of Statistics, Science Center 705, 9-11:30am, Wednesday, November 18, 2015:

This talk summarizes a paper -- Gary King and Richard Nielsen. 2015. “Why Propensity Scores Should Not Be Used for Matching” -- with this abstract:  Researchers use propensity score matching (PSM) as a data preprocessing step to selectively prune units prior to applying a model to estimate a causal effect. The goal of PSM is to reduce imbalance in the chosen pre-treatment covariates between the treated and control groups, thereby reducing the

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Reverse-Engineering Censorship in China, at IARPA seminar on "Science, Intelligence, and Security," Virginia Tech Research Center, Monday, November 16, 2015:

Chinese government censorship of social media constitutes the largest selective suppression of human communication in recorded history. In three ways, we show, paradoxically, that this large system also leaves large footprints that reveal a great deal about itself and the intentions of the government. First is an observational study where we download all social media posts before the Chinese government can read and censor those they deem objectionable, and then detect from a network of computers all over the world which are censored. Second, we conduct

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Reverse-Engineering Censorship in China, at Ohio State University, Mershon Center for International Security Studies, Thursday, October 22, 2015:

Chinese government censorship of social media constitutes the largest selective suppression of human communication in recorded history. In three ways, we show, paradoxically, that this large system also leaves large footprints that reveal a great deal about itself and the intentions of the government. First is an observational study where we download all social media posts before the Chinese government can read and censor those they deem objectionable, and then detect from a network of computers all over the world which are censored. Second, we conduct

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Why Propensity Scores Should Not Be Used For Matching, at Department of Epidemiology, Harvard T.H. Chan School of Public Health, Thursday, October 15, 2015:

This talk summarizes a paper -- Gary King and Richard Nielsen. 2015. “Why Propensity Scores Should Not Be Used for Matching” -- with this abstract:  Researchers use propensity score matching (PSM) as a data preprocessing step to selectively prune units prior to applying a model to estimate a causal effect. The goal of PSM is to reduce imbalance in the chosen pre-treatment covariates between the treated and control groups, thereby reducing the

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Explaining Systematic Bias and Nontransparency in US Social Security Administration Forecasts, at Inaugural Distinguished Lecture, Institute for Social Science, UC-Davis, Wednesday, October 7, 2015:


The accuracy of U.S. Social Security Administration (SSA) demographic and financial forecasts is crucial for the solvency of its Trust Funds, government programs comprising greater than 50% of all federal government expenditures, industry decision making, and the evidence base of many scholarly articles. Forecasts are also essential for scoring policy proposals put forward by both political parties or anyone else. Because SSA makes public little replication information, and uses ad hoc, qualitative, and antiquated statistical forecasting methods

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