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Conference Paper

Identifying confounders using additive noise models

MPS-Authors
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Janzing,  D
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons84134

Peters,  J
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Mooij,  JM
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons84193

Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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https://www.cs.mcgill.ca/~uai2009/
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UAI-2009-Janzing.pdf
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Citation

Janzing, D., Peters, J., Mooij, J., & Schölkopf, B. (2009). Identifying confounders using additive noise models. In N. Bilmes, A. Ng, & D. McAllester (Eds.), 25th Conference on Uncertainty in Artificial Intelligence (UAI 2009) (pp. 249-257). Corvallis, OR, USA: AUAI Press.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-C481-3
Abstract
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.