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Accurate Causal Inference on Discrete Data

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conference contribution
posted on 2023-11-29, 18:09 authored by Kailash Budhathoki, Jilles VreekenJilles Vreeken
Additive Noise Models (ANMs) provide a theoretically sound approach to inferring the most likely causal direction between pairs of random variables given only a sample from their joint distribution. The key assumption is that the effect is a function of the cause, with additive noise that is independent of the cause. In many cases ANMs are identifiable. Their performance, however, hinges on the chosen dependence measure, the assumption we make on the true distribution. In this paper we propose to use Shannon entropy to measure the dependence within an ANM, which gives us a general approach by which we do not have to assume a true distribution, nor have to perform explicit significance tests during optimization. The information-theoretic formulation gives us a general, efficient, identifiable, and, as the experiments show, highly accurate method for causal inference on pairs of discrete variables-achieving (near) 100% accuracy on both synthetic and real data.

History

Preferred Citation

Kailash Budhathoki and Jilles Vreeken. Accurate Causal Inference on Discrete Data. In: IEEE International Conference on Data Mining (ICDM). 2018.

Primary Research Area

  • Empirical and Behavioral Security

Name of Conference

IEEE International Conference on Data Mining (ICDM)

Legacy Posted Date

2019-06-07

Open Access Type

  • Unknown

BibTeX

@inproceedings{cispa_all_2905, title = "Accurate Causal Inference on Discrete Data", author = "Budhathoki, Kailash and Vreeken, Jilles", booktitle="{IEEE International Conference on Data Mining (ICDM)}", year="2018", }

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