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Estimating Causal Direction and Confounding of Two Discrete Variables

Chalupka, Krzysztof and Eberhardt, Frederick and Perona, Pietro (2016) Estimating Causal Direction and Confounding of Two Discrete Variables. . (Unpublished)

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We propose a method to classify the causal relationship between two discrete variables given only the joint distribution of the variables, acknowledging that the method is subject to an inherent baseline error. We assume that the causal system is acyclicity, but we do allow for hidden common causes. Our algorithm presupposes that the probability distributions P(C) of a cause C is independent from the probability distribution P(E∣C) of the cause-effect mechanism. While our classifier is trained with a Bayesian assumption of flat hyperpriors, we do not make this assumption about our test data. This work connects to recent developments on the identifiability of causal models over continuous variables under the assumption of "independent mechanisms". Carefully-commented Python notebooks that reproduce all our experiments are available online at

Item Type:Report or Paper (Discussion Paper)
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URLURL TypeDescription Paper ItemPython notebooks
Chalupka, Krzysztof0000-0002-1225-2112
Perona, Pietro0000-0002-7583-5809
Record Number:CaltechAUTHORS:20190327-085917121
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:94200
Deposited By: George Porter
Deposited On:27 Mar 2019 17:13
Last Modified:03 Oct 2019 21:01

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