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Outcome estimation of treatments for target individuals is an importantfoundation for decision making based on causal relations. Most existing outcomeestimation methods deal with binary or multiple-choice treatments; however, insome applications, the number of treatments can be significantly large, whilethe treatments themselves have rich information. In this study, we consideredone important instance of such cases: the outcome estimation problem ofgraph-structured treatments such as drugs. Owing to the large number ofpossible treatments, the counterfactual nature of observational data thatappears in conventional treatment effect estimation becomes more of a concernfor this problem. Our proposed method, GraphITE (pronounced "graphite") learnsthe representations of graph-structured treatments using graph neural networkswhile mitigating observation biases using Hilbert-Schmidt IndependenceCriterion regularization, which increases the independence of therepresentations of the targets and treatments. Experiments on two real-worlddatasets show that GraphITE outperforms baselines, especially in cases with alarge number of treatments.

GraphITE: Estimating Individual Effects of Graph-structured Treatments