Learning Individual Treatment Effects from Networked Observational Data
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With convenient access to observational data, learning individual causal effects from such data draws more attention in many influential research areas such as economics, healthcare, and education. For example, we aim to study how a medicine (treatment) would affect the health condition (outcome) of a certain patient. To validate causal inference from observational data, we need to control the influence of confounders - the variables which causally influence both the treatment and the outcome. Along this line, existing work for learning individual treatment effect overwhelmingly relies on the assumption that there are no hidden confounders. However, in real-world observational data, this assumption is untenable and can be unrealistic. In fact, an important fact ignored by them is that observational data can come with network information that can be utilized to infer hidden confounders. For example, in an observational study of the individual treatment effect of a medicine, instead of randomized experiments, the medicine is assigned to individuals based on a series of factors. Some factors (e.g., socioeconomic status) are hard to measure directly and therefore become hidden confounders of observational datasets. Fortunately, the socioeconomic status of an individual can be reflected by whom she is connected in social networks. With this fact in mind, we aim to exploit the network structure to recognize patterns of hidden confounders in the task of learning individual treatment effects from observational data. In this work, we propose a novel causal inference framework, the network deconfounder, which learns representations of confounders by unraveling patterns of hidden confounders from the network structure between instances of observational data. Empirically, we perform extensive experiments to validate the effectiveness of the network deconfounder on various datasets.
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