Identification and estimation of treatment and interference effects in observational studies on networks

Understand

Causal inference on a population of units connected through a network often presents technical challenges, including how to account for interference.

• In the presence of local interference, for instance, potential outcomes of a unit depend on its treatment as well as on the treatments of other local units, such as its neighbors according to the network.
• In observational studies, a further complication is that the typical unconfoundedness assumption must be extended - say, to include the treatment of neighbors, and indi- vidual and neighborhood covariates - to guarantee identification and valid inference.
• Here, we propose new estimands that define treatment and interference effects.