A0637
Title: Regression discontinuity designs under interference
Authors: Tiziano Arduini - Tor Vergata University of Rome (Italy) [presenting]
Laura Forastiere - Yale University (United States)
Elena Dal Torrione - Yale University (United States)
Abstract: The continuity-based framework is extended to regression discontinuity designs (RDDs) to identify and estimate causal effects in the presence of interference when units are connected through a network. In this setting, the assignment to an "effective treatment", which comprises the individual treatment and a summary of the treatment of interfering units (e.g., friends, classmates), is determined by the unit's score and the scores of other interfering units, leading to a multiscore RDD with potentially complex, multidimensional boundaries. These boundaries are characterized, and generalized continuity assumptions are derived to identify the proposed causal estimands, i.e., point and boundary causal effects. Additionally, a distance-based nonparametric estimator is developed, its asymptotic properties are derived under restrictions on the network degree distribution, and a novel variance estimator is introduced that accounts for network correlation. Finally, the methodology is applied to the PROGRESA/Oportunidades dataset to estimate the direct and indirect effects of receiving cash transfers on children's school attendance.
