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arXiv · 2011.04826

Reducing bias in difference-in-differences models using entropy balancing

Matthew Cefalu·Brian G. Vegetabile·Michael Dworsky·Christine Eibner·Federico Girosi
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Abstract

This paper illustrates the use of entropy balancing in difference-in-differences analyses when pre-intervention outcome trends suggest a possible violation of the parallel trends assumption. We describe a set of assumptions under which weighting to balance intervention and comparison groups on pre-intervention outcome trends leads to consistent difference-in-differences estimates even when pre-intervention outcome trends are not parallel. Simulated results verify that entropy balancing of pre-intervention outcomes trends can remove bias when the parallel trends assumption is not directly satisfied, and thus may enable researchers to use difference-in-differences designs in a wider range of observational settings than previously acknowledged.

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Matthew Cefalu, Brian G. Vegetabile, Michael Dworsky, Christine Eibner, Federico Girosi. 2020-11-09. Reducing bias in difference-in-differences models using entropy balancing. https://arxiv.org/abs/2011.04826

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