  While much attention has been given to the problem of estimating the effect
of discrete interventions from observational data, relatively little work has
been done in the setting of continuous-valued interventions, such as treatments
associated with a dosage parameter. In this paper, we tackle this problem by
building on a modification of the generative adversarial networks (GANs)
framework. Our model, SCIGAN, is flexible and capable of simultaneously
estimating counterfactual outcomes for several different continuous
interventions. The key idea is to use a significantly modified GAN model to
learn to generate counterfactual outcomes, which can then be used to learn an
inference model, using standard supervised methods, capable of estimating these
counterfactuals for a new sample. To address the challenges presented by
shifting to continuous interventions, we propose a novel architecture for our
discriminator - we build a hierarchical discriminator that leverages the
structure of the continuous intervention setting. Moreover, we provide
theoretical results to support our use of the GAN framework and of the
hierarchical discriminator. In the experiments section, we introduce a new
semi-synthetic data simulation for use in the continuous intervention setting
and demonstrate improvements over the existing benchmark models.
