Indirect effects and causal inference: reconsidering regression discontinuity

Causal inference models, like regression discontinuity (RD) design, rely upon some variation of the no-interference assumption, where peer effects or spatial spillovers are null. Given the increased application of network, spatial, and peer effects models, this paper reconsiders RD design when this assumption is not satisfied, yielding indirect effects of the treatment in addition to the traditionally measured direct effects. Using a combination of residualization and numeric integration we develop a method—using the Spatial Durbin Framework—which retains the full adjacency matrix and allows for a full accounting of these cross-sectional interactions. As an application, we revisit a well-known RD design using U.S. House of Representatives election results from 1945–1995, finding close election wins have substantial indirect effects which previously were unaccounted.