Spillover mechanism selection via exposure mapping

Music City Center 

Consider an experimental study on a population of units connected by a network. A common tool deployed in the literature to define and estimate spillover effects is of exposure mappings. These mappings reduce the complexity of the estimand to a lower dimension, facilitating identifiability. It is assumed that this mapping is correctly specified leaving the choice of the exposure mapping to the analyst. This makes the estimators of the spillover effect, like the Horvitz-Thompson estimator, vulnerable to biases. While it has been shown that these estimators are robust to some controlled forms of misspecification, there is a dearth of methodological advancement in empirically investigating appropriate exposure mappings. We propose to facilitate a model selection procedure that helps in choosing the most suited exposure mapping given a choice of candidate exposure mappings in an experimental setting.

Causal Inference

Network Interference

Model Selection

Peer Effects

Randomized Trials 

IMS