DeepMatch: Balancing Deep Covariate Representations for Causal Inference Using Adversarial Training
This addresses a methodological gap in causal inference for researchers and practitioners dealing with complex data like images, though it appears incremental as an adaptation of adversarial training to this domain.
The paper tackles the problem of achieving optimal covariate balance for causal inference from observational data with rich covariates and complex relationships, proposing a method based on adversarial training of weighting and discriminator networks that enables strong causal analyses in challenging settings.
We study optimal covariate balance for causal inferences from observational data when rich covariates and complex relationships necessitate flexible modeling with neural networks. Standard approaches such as propensity weighting and matching/balancing fail in such settings due to miscalibrated propensity nets and inappropriate covariate representations, respectively. We propose a new method based on adversarial training of a weighting and a discriminator network that effectively addresses this methodological gap. This is demonstrated through new theoretical characterizations of the method as well as empirical results using both fully connected architectures to learn complex relationships and convolutional architectures to handle image confounders, showing how this new method can enable strong causal analyses in these challenging settings.