A General Method for Detecting Interference Between Units in Randomized Experiments
Interference between units may pose a threat to unbiased causal inference in randomized controlled experiments. Although the assumption of no interference is essential for causal inference, few options are available for testing this assumption. This paper presents the first reliable ex post method for detecting interference between units in randomized experiments. Naive estimators of interference that attempt to exploit the proximity of units may be biased because simple randomization of units into treatment does not imply simple randomization of proximity to treated units. However, through a randomization-based approach, the confounding associated with these naive estimators may be circumvented entirely. With a test statistic of the analyst's choice, a conditional randomization test allows for the calculation of the exact significance of the causal dependence of outcomes on the treatment status of other units. The efficacy and robustness of the method is demonstrated through simulation studies and, using this method, interference between units is detected in a field experiment designed to assess the effect of mailings on voter turnout.
Rubin Causal Model
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