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An increasing number of studies exploit the occurrence of unexpected events during the fieldwork of public opinion surveys to estimate causal effects. In this paper, we discuss the use of this identification strategy based on unforeseen and salient events that split the sample of respondents into treatment and control groups: the Unexpected Event during Survey Design. In particular, we focus on the assumptions under which unexpected events can be exploited to estimate causal effects and we discuss potential threats to identification, paying especial attention to the observable and testable implications of these assumptions. We propose a series of best practices in the form of various estimation strategies and robustness checks that can be used to lend credibility to the causal estimates. Drawing on data from the European Social Survey, we illustrate the discussion of this method with an original study of the impact of the Charlie Hebdo terrorist attacks (Paris, 01/07/2015) on French citizens’ satisfaction with their national government.

Unexpected Event during Survey Design: Promise and Pitfalls for Causal Inference