Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning
This work addresses a long-standing obstacle in causal inference for researchers and practitioners dealing with observational data, though it is incremental as it extends existing ADMG methods with non-linear assumptions.
The paper tackled the problem of causal reasoning with latent confounders by developing a gradient-based method to learn acyclic directed mixed graphs (ADMGs) with non-linear functional relations from observational data, demonstrating competitive performance on synthetic and real-world datasets.
Latent confounding has been a long-standing obstacle for causal reasoning from observational data. One popular approach is to model the data using acyclic directed mixed graphs (ADMGs), which describe ancestral relations between variables using directed and bidirected edges. However, existing methods using ADMGs are based on either linear functional assumptions or a discrete search that is complicated to use and lacks computational tractability for large datasets. In this work, we further extend the existing body of work and develop a novel gradient-based approach to learning an ADMG with non-linear functional relations from observational data. We first show that the presence of latent confounding is identifiable under the assumptions of bow-free ADMGs with non-linear additive noise models. With this insight, we propose a novel neural causal model based on autoregressive flows for ADMG learning. This not only enables us to determine complex causal structural relationships behind the data in the presence of latent confounding, but also estimate their functional relationships (hence treatment effects) simultaneously. We further validate our approach via experiments on both synthetic and real-world datasets, and demonstrate the competitive performance against relevant baselines.