Presentations

Reverse Engineering Chinese Censorship, at Harvard University IT Summit, Cambridge MA, Thursday, June 5, 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

Read more about Reverse Engineering Chinese Censorship
Reverse Engineering Chinese Censorship, at Wesleyan University, Thursday, March 6, 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

Read more about Reverse Engineering Chinese Censorship
Simplifying Causal Inference, at University of South Carolina, Friday, February 28, 2014:

We propose a simplified approach to matching for causal inference that simultaneously optimizes both balance (between the treated and control groups) and matched sample size. This procedure resolves two widespread (bias-variance trade off-related) tensions in the use of this powerful and popular methodology. First, current practice is to run a matching method that maximizes one balance metric (such as a propensity score or average Mahalanobis distance), but then to check whether it succeeds with respect to a different balance metric for which it was not designed (such as differences

Read more about Simplifying Causal Inference
Reverse Engineering Chinese Censorship, at University of South Carolina, Thursday, February 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 scale

Read more about Reverse Engineering Chinese Censorship
Reverse Engineering Chinese Censorship, at Georgetown University, Friday, February 7, 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 scale

Read more about Reverse Engineering Chinese Censorship
The Balance-Sample Size Frontier in Matching Methods for Causal Inference, at University of Michigan, Friday, January 24, 2014:

We propose a simplified approach to matching for causal inference that simultaneously optimizes both balance (between the treated and control groups) and matched sample size. This procedure resolves two widespread (bias-variance trade off-related) tensions in the use of this powerful and popular methodology. First, current practice is to run a matching method that maximizes one balance metric (such as a propensity score or average Mahalanobis distance), but then to check whether it succeeds with respect to a different balance metric for which it was not designed (such as differences in

Read more about The Balance-Sample Size Frontier in Matching Methods for Causal Inference
Reverse Engineering Chinese Censorship, at Asian Political Methodology Meetings, Tokyo, Monday, January 6, 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 scale randomized

Read more about Reverse Engineering Chinese Censorship
Reverse Engineering Chinese Censorship, at MIT Operations Research Center, Thursday, November 21, 2013:

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 scale randomized

Read more about Reverse Engineering Chinese Censorship

Pages