Presentations

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, thereby reducing the degree of model dependence and Read more about Why Propensity Scores Should Not Be Used For Matching

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 Read more about Reverse-Engineering Censorship in China

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 Read more about Simplifying Matching Methods for Causal Inference

Reverse-Engineering Censorship in China, at Harvard Graduate Commons Program, Wednesday, February 11, 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 a large Read more about Reverse-Engineering Censorship in China

Simplifying Matching Methods for Causal Inference, at Princeton University, Center for Statistics and Machine Learning, Friday, February 6, 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 Read more about Simplifying Matching Methods for Causal Inference

Reverse-Engineering Censorship in China, at American University, Friday, January 30, 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 a large Read more about Reverse-Engineering Censorship in China

Reverse-Engineering Censorship in China, at Stanford University, Computer Science, Data Science, Friday, January 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 a large Read more about Reverse-Engineering Censorship in China

Simplifying Matching Methods for Causal Inference, at Stanford University, Department of Political Science, Wednesday, January 14, 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 Read more about Simplifying Matching Methods for Causal Inference

Reverse-Engineering Censorship in China, at Harvard Kennedy School, Inequality Seminar, Monday, October 27, 2014:

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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