Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks

Understand

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.