Identifying Structural Biases from Causal Mechanism Shifts
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Computer Science > Machine Learning
Title:Identifying Structural Biases from Causal Mechanism Shifts
Abstract:Causal discovery methods commonly assume that all data is independently and identically distributed (i.i.d.) and that there are no unmeasured variables affecting the system. In practice, these assumptions are often violated, leading to inaccurate inference. In this paper, we study how to identify hidden confounding and selection biases from causal mechanism shifts. In particular, we show that structural biases lead to dependent mechanism shifts. That is, by considering for which variables the mechanisms change given data from different environments, we can tell which variables are unbiased, which are subject to hidden confounding, and which are undergoing selection bias. We formalize this into an empirically testable criterion based on mutual information, and show under which conditions it identifies structural biases. To tell which nodes are subject to what kind of bias, we introduce the StruBI algorithm. Experiments on synthetic and real-world data show that StruBI works well in practice, accurately recovering affected variable sets and types of biases, outperforming the state-of-the-art by a wide margin.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.18834 [cs.LG] |
| (or arXiv:2606.18834v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.18834
arXiv-issued DOI via DataCite (pending registration)
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