Estimation and testing for two-stage experiments in the presence of interference

Guillaume Basse, Harvard

Wednesday, February 7, 2018 - 3:30pm

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 estimation and 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. First, I show how to construct unbiased estimators for a large class of estimands and discuss properties of these estimators. Second, I demonstrate how to construct powerful tests for non-sharp null hypotheses. I use these methods 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

Seminars generally take place in 116 Old Chemistry Building on Fridays from 3:30 - 4:30 pm. For additional information contact: or phone 919-684-8029. Sorry, but we do not have reprints available. Please feel free to contact the authors by email for follow-up information, articles, etc. Reception following seminar in 211 Old Chemistry

Old Chemistry 116

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