Presentations

Simplifying Matching Methods for Causal Inference, at Harvard University, Department of Biostatistics, HIV Working Group, 11/17/2017, Friday, November 17, 2017:
This presentation shows how to use matching to ameliorate model dependence -- where small, indefensible changes in model specification have large impacts on our conclusions. We introduce matching methods that are simpler, more powerful, and easier to understand. We also show that the most commonly used existing method, propensity score matching, should rarely be used. Easy-to-use software is available to implement all methods discussed.
Simplifying Matching Methods for Causal Inference, at Harvard Health Policy and Insurance Research Seminar, Monday, October 16, 2017:
This presentation shows how to use matching to ameliorate model dependence -- where small, indefensible changes in model specification have large impacts on our conclusions. We introduce matching methods that are simpler, more powerful, and easier to understand. We also show that the most commonly used existing method, propensity score matching, should rarely be used. Easy-to-use software is available to implement all methods discussed.
Reverse Engineering Chinese Government Information Controls, at Paul and Marica Wythes Center on Contemporary China, Princeton University, Wednesday, October 11, 2017:
This talk is based on this paper (in the current issue of the American Political Science Review), by Jen Pan, Molly Roberts, and me, along with a brief summary of our previous work (in Science here, and the American Poltiical Science Review ... Read more about Reverse Engineering Chinese Government Information Controls
How the Chinese Government Fabricates Social Media Posts for Strategic Distraction, not Engaged Argument, at Harvard University and National Taiwan University, Friday, September 29, 2017:
This talk is based on this paper (in the current issue of the American Political Science Review), by Jen Pan, Molly Roberts, and me, along with a brief summary of our previous work (2014 in Science here, and 2013 in the American Poltiical Science Review ... Read more about How the Chinese Government Fabricates Social Media Posts for Strategic Distraction, not Engaged Argument
Big Data is Not About the Data!, at Abt Associates, Thursday, September 28, 2017:
The spectacular progress the media describes as "big data" has little to do with the growth of 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 merely makes data analysis harder. The real revolution isn't about the data, it is about the stunning progress in the statistical and other methods of extracting insights from the data. I illustrate these points with a wide range of examples from research I've participated in, including ... Read more about Big Data is Not About the Data!
Fabricating News In Chinese Social Media, at Congress of the Mexican Political Science Association, University of Quintana Roo, Cancun, Mexico, Friday, September 15, 2017:

This talk is based on this paper (in the current issue of the American Political Science Review), by Jen Pan, Molly Roberts, and me, along with a brief summary of our previous work (2014 in Science here, and 2013 in the American Poltiical Science Review ...

Read more about Fabricating News In Chinese Social Media
Big Data Reveals Made Up Data: How the Chinese Government Fabricates Social Media Posts for Strategic Distraction, not Engaged Argument, at University of Texas at Austin, Thursday, September 7, 2017
This talk is based on this paper (forthcoming in the American Political Science Review), by Jen Pan, Molly Roberts, and me, along with a brief summary of our previous work (2014 in Science here, and 2013 in the APSR here).... Read more about Big Data Reveals Made Up Data: How the Chinese Government Fabricates Social Media Posts for Strategic Distraction, not Engaged Argument
Detecting and Reducing Model Dependence in Causal Inference, at Peking University, Wednesday, July 26, 2017:

This presentation discusses methods of detecting counterfactuals (predictions, what if questions, and casual inferences) far enough from the data that any inferences based on it will yield highly model dependent inferences -- where small, indefensible changes in a model specification have large impacts on our conclusions. The talk also shows how to ameliorate many situations like this via  matching for causal inference. We introduce matching methods that are simpler, more powerful, and easier to understand. We also show that the most commonly used...

Read more about Detecting and Reducing Model Dependence in Causal Inference

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