This model has been used to distinguish between causes and effects for the “Cause-effect pairs” task (Mooij et al., 2008) in the second causality chal- lenge, ...
May 9, 2012 · We show that this model is identifiable in most cases; by enumerating all possible situations in which the model is not identifiable, we provide ...
Jun 18, 2009 · In this paper, we conduct a systematic investigation on its identifiability in the two-variable case. We show that this model is identifiable in ...
It is shown that this post-nonlinear causal model is identifiable in most cases; by enumerating all possible situations in which the model is not ...
They assume that the effect is governed by the cause and an additive noise, and the causal inference is done by finding the direction that admits such a model.
Zhang, K., & Hyvärinen, A. (2009, June). On the Identifiability of the Post-Nonlinear Causal Model. In 25th Conference on Uncertainty in Artificial Intelligence ...
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Representing each variable as a function of its direct causes, an independent disturbance, and a post-nonlinear distortion, PNL can distinguish between causes ...
On the identifiability of the post-nonlinear causal model. K Zhang , Aapo Hyvärinen · Department of Mathematics and Statistics. Research output: Chapter in Book ...
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Abstract: By taking into account the nonlinear effect of the cause, the inner noise effect, and the measurement distortion effect in the observed variables, ...
We study the identifiability and estimation of functional causal models under selection bias, with a focus on the situation where the.