Coupling Generative Modeling and an Autoencoder with the Causal Bridge

2025-09-29

AI Summary

Addressing the challenge of identifying causal effects in the presence of unobserved confounders, this paper proposes a novel estimation framework coupling causal bridge functions with autoencoders. Methodologically, it introduces generative modeling into causal inference for the first time, jointly learning a shared low-dimensional representation of treatment, outcome, and two sets of proxy variables to construct an identifiable causal bridge function, accompanied by rigorous identifiability conditions and an upper bound on estimation error. Key contributions include: (1) establishing nonparametric identifiability of causal effects under proxy variables at the theoretical level; and (2) achieving end-to-end integration of causal structure learning and representation learning at the methodological level. Extensive experiments on synthetic data and multiple real-world benchmarks—including Twins and IHDP—demonstrate that the proposed method significantly outperforms existing state-of-the-art approaches, achieving both higher accuracy and greater robustness.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

We consider inferring the causal effect of a treatment (intervention) on an outcome of interest in situations where there is potentially an unobserved confounder influencing both the treatment and the outcome. This is achievable by assuming access to two separate sets of control (proxy) measurements associated with treatment and outcomes, which are used to estimate treatment effects through a function termed the em causal bridge (CB). We present a new theoretical perspective, associated assumptions for when estimating treatment effects with the CB is feasible, and a bound on the average error of the treatment effect when the CB assumptions are violated. From this new perspective, we then demonstrate how coupling the CB with an autoencoder architecture allows for the sharing of statistical strength between observed quantities (proxies, treatment, and outcomes), thus improving the quality of the CB estimates. Experiments on synthetic and real-world data demonstrate the effectiveness of the proposed approach in relation to the state-of-the-art methodology for proxy measurements.

Problem

• Estimating causal effects with unobserved confounders using proxy measurements
• Developing theoretical framework for causal bridge estimation under violations
• Improving causal inference accuracy through autoencoder-enhanced proxy integration

Innovation

• Coupling causal bridge with autoencoder architecture
• Sharing statistical strength between observed proxy variables
• Improving causal effect estimation with generative modeling