Seminars

Other UCLA departments frequently hold seminars related to Statistics and of likely of interest to our members. Here are links to UCLA Biostatistics seminars and UCLA Biomath seminars:
https://www.biostat.ucla.edu/2018-seminars
http://www.biomath.ucla.edu/seminars/

Join the UCLA Statistics seminars mailing list by sending an email to sympa@sympa.cts.ucla.edu with “subscribe stat_seminars” (without quotation marks) in the subject field and the message body blank. This needs to be done from the address that is to be subscribed. After doing that please respond to the email that you receive. An automated email will be sent which confirms that you have been added.

You may be receiving our seminar emails because you are subscribed to our seminars mailing list (or one of our other mailing lists). You can determine which is the case by looking at the subject line of a seminar email. You may unsubscribe from the seminar mailing list by sending an email to sympa@sympa.cts.ucla.edu with “unsubscribe stat_seminars” (without quotation marks) in the subject field and the message body blank. This needs to be done from the address that is subscribed. After sending that email please follow the directions in the email response that you receive. If you are getting our seminar emails because of a subscription to one of our other mailing lists then the word “seminars” in the subject field must have the appropriate replacement.

Tuesday, 11/20/2018, Time: 2:00PM – 3:00PM
Statistics Weekly Seminar
Conditional Randomization Tests of Causal Effects with Interference Between Units

Physics and Astronomy Building Room 1434A
Guillaume Basse
UC Berkeley

Many important causal questions concern interactions between units, also known as interference. Examples include interactions between individuals in households, students in schools, and firms in markets. Standard analyses that ignore interference can often break down in this setting: estimators can be badly biased, while classical randomization tests can be invalid. In this talk, I present recent results on testing for two-stage experiments, which are powerful designs for assessing interference. In these designs, whole clusters (e.g., households, schools, or graph partitions) are assigned to treatment or control; then units within each treated cluster are randomly assigned to treatment or control. I demonstrate how to construct powerful tests for non-sharp null hypotheses and use these results to analyze a two-stage randomized trial evaluating an intervention to reduce student absenteeism in the School District of Philadelphia. I discuss some extensions to more general forms of interference, as well as some current challenges. Paper here: https://arxiv.org/abs/1709.08036

I am currently a postdoctoral fellow in the Statistics Department at UC Berkeley where I am advised by Peng Ding. My research focuses on Causal Inference and Design of Experiments in the presence of interference. I got my PhD in Statistics at Harvard in 2018, under the supervision of Edo Airoldi. Before coming to Harvard I attended the Ecole Centrale Paris, where I studied Applied Mathematics and Engineering. I have lived in France, Israel, the US and Senegal, where I was born. I will start as an assistant professor in the MS&E and Statistics departments at Stanford in July 2019.