Exploring the causal order of binary variables via exponential hierarchies of Markov kernels
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BibTeX
@MISC{Sun_exploringthe,
author = {Xiaohai Sun and Dominik Janzing},
title = {Exploring the causal order of binary variables via exponential hierarchies of Markov kernels},
year = {}
}
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Abstract
Abstract. We propose a new algorithm for estimating the causal structure that underlies the observed dependence among n (n ≥ 4) binary variables X1,..., Xn. Our inference principle states that the factorization of the joint probability into conditional probabilities for Xj given X1,..., Xj−1 often leads to simpler terms if the order of variables is compatible with the directed acyclic graph representing the causal structure. We study joint measures of OR/AND gates and show that the complexity of the conditional probabilities (the so-called Markov kernels), defined by a hierarchy of exponential models, depends on the order of the variables. Some toy and real-data experiments support our inference rule. 1
Citations
| 766 | Causality: Models, Reasoning, and Inference - Pearl |
| 96 | Combinatorial methods in density estimation - Devroye, Lugosi - 2001 |
| 23 | A useful convergence theorem for probability distributions - Scheffé, H - 1947 |
| 7 | Causal inference by choosing graphs with most plausible Markov kernels - Sun, Janzing, et al. - 2006 |
| 6 | Discovery of non-Gaussian linear causal models using ICA - Shimizu, Hyvärinen, et al. - 2005 |
