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We propose an easy-to-use adjustment estimator for the effect of a treatmentbased on observational data from a single (social) network of units. Theapproach allows for interactions among units within the network, calledinterference, and for observed confounding. We define a simplified causal graphthat does not differentiate between units, called generic graph. Using validadjustment sets determined in the generic graph, we can identify the treatmenteffect and build a corresponding estimator. We establish the estimator'sconsistency and its convergence to a Gaussian limiting distribution at theparametric rate under certain regularity conditions that restrict the growth ofdependencies among units. We empirically verify the theoretical properties ofour estimator through a simulation study and apply it to estimate the effect ofa strict facial-mask policy on the spread of COVID-19 in Switzerland.

A Graphical Approach to Treatment Effect Estimation with Observational Network Data