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Difference-in-Differences in the Presence of Unknown Interference
(joint with Javier Viviens)
The stable unit treatment value (SUTVA) is a crucial assumption in the Difference-in-Differences (DiD) research design. It rules out hidden versions of treatment and any sort of interference and spillover effects across units. Even if this is a strong assumption, it has not received much attention from DiD practitioners and, in many cases, it is not even explicitly stated as an assumption, especially the no-interference assumption. In this technical note, we investigate what the DiD estimand identifies in the presence of unknown interference. We show that the DiD estimand identifies a contrast of causal effects, but it is not informative on any of these causal effects separately, without invoking further assumptions. Then, we explore different sets of assumptions under which the DiD estimand becomes informative about specific causal effects. We illustrate these results by revisiting the seminal paper on minimum wages and employment by Card and Krueger (1994).
Short bio
Fabrizia Mealli is currently Professor of Econometrics at the Department of Economics, European University Institute, on leave as Professor of Statistics from the University of Florence. She held visiting positions at the Harvard Statistics and Biostatistics Departments in 2001, 2015, and 2017.
Her research focuses on statistical and econometric methods for causal inference in experimental and observational settings, estimation techniques, simulation methods, missing data, and Bayesian inference, with applications to the social and biomedical sciences. She is an Elected Fellow of the American Statistical Association (ASA), sits on the Steering Committee of the European Causal Inference Meeting (EUROCIM), and is past-President of the Society for Causal Inference (SCI).
She is an Associate Editor for Biometrika, the Journal of the American Statistical Association T&M, The Annals of Applied Statistics, and Observational Studies. Since 2001, she has been teaching Causal Inference in International Schools and in Master and PhD programmes around the world.
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