Double Generative Learning for Causal Inference

Music City Center 

With the rapid development of artificial intelligence, causal inference with observational data has drawn much attention in various scientific domains. A key challenge in this area is the often-violated ignorability assumption, which is critical for unbiased estimation of causal effects, but very hard to check in practice. To address this challenge, we develop Double Generative Learning (DGL), a novel approach that leverages the capabilities of generative adversarial networks (GANs) for robust causal inference under the violation of ignorability. By employing a delicate dual GANs structure, DGL emulates data akin to randomized controlled trials (RCTs) solely based on observational studies, circumventing the biases introduced by unobserved confounders. This methodology not only proposes an elegant solution to the issue of ignorability violation by achieving minimax optimality in robustness but also adeptly manages high-dimensional and complex data structures. Theoretical analysis reveals DGL's capacity to bypass the curse of dimensionality by exploiting the inherent low-dimensional submanifold structures in the data. Through extensive simulation studies and analyses of real-world datasets, DGL's empirical superiority in facilitating robust causal inference under adverse conditions is comprehensively
demonstrated.

Average treatment effect; Observational study; Ignorability; Randomized controlled trials; Curse of dimensionality; Generative adversarial networks;