Instrumental variable (IV) methods are powerful for causal effect estimation with unmeasured confounding, but in practice researchers often face a set of candidate IVs whose validity is difficult to determine from observational data. This paper studies the problem of testing the validity of IV sets under Causal Additiv...
Xi-Chen Guo, Feng Xie, Bingbing Tang et al.· 0 citations
Uncovering latent variables and their causal relations from observed data is a fundamental yet challenging problem. Existing methods often rely on restrictive assumptions, such as linear relations or invertible mixing functions. To better address this problem under general nonlinear mixing procedures, we propose a cond...
Zijian Li, Ruichu Cai, Feng Xie et al.· 0 citations
Model-based reinforcement learning (MBRL), which learns environment dynamics to generate synthetic experience, is a promising approach to sample-efficient decision making. Numerous methods have been developed to improve dynamics prediction and policy optimization for MBRL through uncertainty estimation, model regulariz...
Ze-Feng Liang, Jie Qiao, Ruichu Cai et al.· 0 citations
LoCaLS is proposed, a local causal structure learning algorithm that is sound and complete under standard assumptions and identifies the same direct causes and effects of a target variable as those identifiable by global causal discovery methods, while allowing for latent variables and selection bias.
Zheng Li, Hao Zhang, Ruxin Wang et al.· arXiv.org· 1 citation
The Causal Discovery Foundation Model (CDFM) is formulated as a unified, general-purpose framework for zero-shot structural inference, and a principled variational framework that treats unknown causal mechanisms as latent variables and mathematically decomposes the intractable marginal likelihood into distinct, tractab...
Jie Qiao, Ruichu Cai, Zijian Li et al.· arXiv.org· 2 citations
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