Causal Inference with Social Interactions: A Structural Break Viewpoint

Music City Center 

We propose a factor-based method for estimating treatment effects in panel data models with spatial interference. We focus on a binary treatment in a non-experimental setting and characterise the potential outcomes by a modified factor model that allows for interference between any two units. We also provide two economic illustrations of this factor model. The estimation of treatment effects is recast as disentangling sub-vectors of factors and loadings from the full vectors and accomplished by exploiting the factor structure implied by the model. Under standard assumptions, the estimator of every individual and time specific treatment effect is proved to be consistent and asymptotically normal as the numbers of units, pre-treatment and post-treatment times go to infinity. We find consistent estimators for the associated asymptotic variances, which leads to asymptotically pivotal inference on the treatment effects. This method can be extended to models with covariates.