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Identifying confounders using additive noise models

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arxiv 1205.2640 v1 pith:OS3P7ZCL submitted 2012-05-09 stat.ML cs.LG

Dominik Janzing , Jonas Peters , Joris Mooij , Bernhard Schoelkopf This is my paper · ORCID
classification stat.MLcs.LG
keywords confoundermethodeffectsadditiveconditionsidentifiablemodelnoise
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We propose a method for inferring the existence of a latent common cause ('confounder') of two observed random variables. The method assumes that the two effects of the confounder are (possibly nonlinear) functions of the confounder plus independent, additive noise. We discuss under which conditions the model is identifiable (up to an arbitrary reparameterization of the confounder) from the joint distribution of the effects. We state and prove a theoretical result that provides evidence for the conjecture that the model is generically identifiable under suitable technical conditions. In addition, we propose a practical method to estimate the confounder from a finite i.i.d. sample of the effects and illustrate that the method works well on both simulated and real-world data.

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  1. When Additive Noise Meets Unobserved Mediators: Bivariate Denoising Diffusion for Causal Discovery

    cs.LG 2025-06 reject novelty 6.0 of 10

    BiDD identifies causal direction by comparing dependence of predicted diffusion noise on the conditioning variable; consistency is proven only for mediator-free ANM, while hidden-mediation performance remains a conjecture.

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