Presentations

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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Why Propensity Scores Should Not Be Used for Matching, at Harvard Medical School, Brigham and Women's Hosptial, Division of Pharmacoepidemiology and Pharmacoeconomics, Wednesday, September 23, 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's Applied Statistics Workshop, at IQSS, Wednesday, September 16, 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 International Methods Colloquium , Friday, September 11, 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 degree of model dependence and...

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Talks on Matching Methods for Causal Inference, at Food and Drug Administration, Washington DC, Tuesday, August 25, 2015:

Two talks on causal inference using matching methods. The first talk is based on King, Gary, and Richard Nielsen. 2015. “Why Propensity Scores Should Not Be Used for Matching”. The second talk is based on these papers:

  • Iacus, Stefano M, Gary King, and Giuseppe Porro. 2011...
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Why Propensity Scores Should Not Be Used For Matching, at Society for Political Methodology, University of Rochester, Friday, July 24, 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...

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Reverse-Engineering Censorship in China, at Harvard Graduate School of Arts and Science Alumni Day, Saturday, April 11, 2015:

Joint talk together with Jennifer Pan.  

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...

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Simplifying Matching Methods for Causal Inference, at MIT, Political Methodology Series, Monday, March 16, 2015:

This talk explains how to make matching methods for causal inference easier to use and more powerful. Applied researchers commonly use matching methods as a data preprocessing step for reducing model dependence and bias, after which they use whatever statistical procedure they would have without matching, such as regression. They routinely ignore the requirement that all matches be exact, and also commonly use ad hoc analyses that iterate between formal matching methods and informal balance and sample size checks. The talk describes 3 papers which (1) offer the first comprehensive...

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