HOME /

Presentations

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:
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...

Read more about Why Propensity Scores Should Not Be Used for Matching
Why Propensity Scores Should Not Be Used For Matching, at Harvard's Applied Statistics Workshop, at IQSS, Wednesday, September 16, 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...

Read more about Why Propensity Scores Should Not Be Used For Matching
Why Propensity Scores Should Not Be Used for Matching, at International Methods Colloquium , Friday, September 11, 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 model dependence and...

Read more about Why Propensity Scores Should Not Be Used for Matching
Talks on Matching Methods for Causal Inference, at Food and Drug Administration, Washington DC, Tuesday, August 25, 2015:
Slides for Talk 1
Slides for Talk 2

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...
Read more about Talks on Matching Methods for Causal Inference
Why Propensity Scores Should Not Be Used For Matching, at Society for Political Methodology, University of Rochester, Friday, July 24, 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...

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:
Slides

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:
Slides

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:
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...

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:
Slides

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

Pages