We present Relational Covariate Adjustment (RCA), a general method for estimating causal effects in relational data. Relational Covariate Adjustment is implemented through two high-level operations: identification of an adjustment set and relational regression adjustment. The former is achieved through an extension of Pearl’s back-door criterion to relational domains. We demonstrate how this extended definition can be used to estimate causal effects in the presence of network interference and confounding. RCA is agnostic to functional form, and it can easily model both discrete and continuous treatments as well as estimate the effects of a wider array of network interventions than existing experimental approaches. We show that RCA can yield robust estimates of causal effects using common regression models without extensive parameter tuning. Through a series of simulation experiments on a variety of synthetic and real- world network structures, we show that causal effects estimated on observational data with RCA are nearly as accurate as those estimated from well-designed network experiments.

Filed under: Deep Learning | Frequent Pattern Mining | Mining Rich Data Types | Graph Mining and Social Networks