A novel causal reduction method is proposed that replaces an arbitrary number of possibly high-dimensional latent confounders with a single latent confounder that lives in the same space as the treatment variable without changing the observational and interventional distributions entailed by the causal model.
Unobserved confounding is a fundamental challenge in causal inference from observational data. This article develops a mixture-learning perspective, viewing latent confounders as sources of heterogeneity that induce mixture structure in observed data. Under suitable structural and identifiability assumptions, recoverin...
Estimating causal effects from observational data is central to science and policy, but the effects are not identified when confounders are unmeasured. Proximal causal inference addresses this problem with proxies of the unmeasured confounders. However, existing proxy-based approaches either designate proxy roles and s...
This work develops a model-free and constraint-query optimal statistical inference framework for causal discovery under latent variables and selection using single-target interventions, and introduces the system-induced subgraph (SIS) to capture the causal relations among system variables while accounting for context v...
Xiao-Tian Hou, Kwangmoon Park, Hong-Zhe Li· 0 citations
This commentary first introduces a Bayesian semiparametric model for causal inference, then presents a sensitivity analysis strategy for the Gaussian process model, which is easily interpretable and avoids restrictive parametric outcome assumptions.
Xin-Yi Xu, S. MacEachern, Bo Lu· Observational Studies· 0 citations
Self-supervised Causal Effects Estimation is proposed, a novel framework that integrates causal priors with self-supervised learning to construct balanced and predictive representations for causal effects estimation that consistently outperforms state-of-the-art methods.
Xin-Shu Li, Shiyi Yang, Venus Haghighi et al.· ACM Transactions on Intellig...· 0 citations
Missing not at random (MNAR) data pose significant challenges for causal inference, particularly when both confounders and the outcome are partially observed. Without additional assumptions beyond those required for causal inference, causal estimands are generally not identifiable under MNAR mechanisms. This paper firs...
