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Discovering and Explaining Systematic Bias and Nontransparency in US Social Security Administration Forecasts, at University of Florida, Department of Political Science, Friday, March 18, 2016:
Slides

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, no one in or out of government has Read more about Discovering and Explaining Systematic Bias and Nontransparency in US Social Security Administration Forecasts

Big Data is Not About the Data!, at University of Florida, Informatics Symposium, Thursday, March 17, 2016:
Slides

In this talk, Gary King explains that 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 just makes data analysis harder. The real revolution isn't about the data, it is about the stunning progress in the statistical methods of extracting insights from the data. He will illustrate these points with a wide Read more about Big Data is Not About the Data!

Why Propensity Scores Should Not Be Used For Matching, at Yale University, MacMillan-CSAP Workshop on Quantitative Research Methods, Thursday, March 10, 2016:
Slides

This talk summarizes a paper -- Gary King and Richard Nielsen. 2016. “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 degree of Read more about Why Propensity Scores Should Not Be Used For Matching

The Next Big [Social Science] Thing, at National Academy of Sciences, Friday, March 4, 2016:
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"Dr. Gary King, NAS member and Professor at Harvard University, will talk about progress in and the future of the Social Sciences, illustrated with a wide range of examples from his research. These examples include forecasting the solvency of Social Security; reverse engineering Chinese censorship; estimating causes of death in developing countries; automated text analysis of billions of social media posts; dataverse, software and protocols his team developed to run the largest archive of social science Read more about The Next Big [Social Science] Thing

Explaining Systematic Bias and Nontransparency in US Social Security Administration Forecasts, at Hellenic American Bankers Association, NYC, Thursday, January 28, 2016:
Slides

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, no one in or out of government has Read more about Explaining Systematic Bias and Nontransparency in US Social Security Administration Forecasts

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

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 a large Read more about Reverse-Engineering Censorship in China

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

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 degree of Read more about Why Propensity Scores Should Not Be Used For Matching

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:
slides

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 degree of Read more about Why Propensity Scores Should Not Be Used For Matching

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

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 a large Read more about Reverse-Engineering Censorship in China

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