Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks
This addresses a gap in causal inference for continuous treatments, such as dosage effects, which is important for fields like medicine and policy, though it is incremental as it builds on existing GAN frameworks.
The paper tackles the problem of estimating effects of continuous-valued interventions from observational data by proposing SCIGAN, a modified GAN model that learns to generate counterfactual outcomes for multiple continuous interventions, and demonstrates improvements over existing benchmarks in experiments.
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.