  Counterfactual evaluation of novel treatment assignment functions (e.g.,
advertising algorithms and recommender systems) is one of the most crucial
causal inference problems for practitioners. Traditionally, randomized
controlled trials (A/B tests) are performed to evaluate treatment assignment
functions. However, such trials can be time-consuming, expensive, and even
unethical in some cases. Therefore, offline counterfactual evaluation of
treatment assignment functions becomes a pressing issue because a massive
amount of observational data is available in today's big data era.
Counterfactual evaluation requires handling the hidden confounders -- the
unmeasured features which causally influence both the treatment assignment and
the outcome. To deal with the hidden confounders, most of the existing methods
rely on the assumption of no hidden confounders. However, this assumption can
be untenable in the context of massive observational data. When such data comes
with network information, the later can be potentially useful to correct hidden
confounding bias. As such, we first formulate a novel problem, counterfactual
evaluation of treatment assignment functions with networked observational data.
Then, we investigate the following research questions: How can we utilize
network information in counterfactual evaluation? Can network information
improve the estimates in counterfactual evaluation? Toward answering these
questions, first, we propose a novel framework, \emph{Counterfactual Network
Evaluator} (CONE), which (1) learns partial representations of latent
confounders under the supervision of observed treatments and outcomes; and (2)
combines them for counterfactual evaluation. Then through extensive
experiments, we corroborate the effectiveness of CONE. The results imply that
incorporating network information mitigates hidden confounding bias in
counterfactual evaluation.
