Estimating Causal Direction and Confounding of Two Discrete Variables

By Krzysztof Chalupka, Frederick Eberhardt, Pietro Perona. 2016

8 citations recorded by the source corpus.

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 $P(E\mid C)$ 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 this http URL

Subfields: Artificial Intelligence, Probabilistic & Bayesian Methods

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Citations in the corpus

An Analysis of the Adaptation Speed of Causal Models (2020, background): “The Causal Direction is Identifiable under the Dense Prior Chalupka et al. (2016) study the prediction of causal direction from observational data under the dense prior assumption.”
Discovering Causal Signals in Images (2016, methodology): “We also build on the growing literature discussing the discovery of causal relationships from observational data [10, 19, 17, 1].”
Causal Regularization (2017, methodology): “This is the key idea of the causality detectors in (Lopez-Paz et al., 2016; Chalupka et al., 2016) described in the rest of this section.”