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Causal Inference on Multivariate and Mixed Type Data

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conference contribution
posted on 2023-11-29, 18:09 authored by Alexander Marx, Jilles VreekenJilles Vreeken
How can we discover whether X causes Y , or vice versa, that Y causes X, when we are only given a sample over their joint distribution? How can we do this such that X and Y can be univariate, multivariate, or of different cardinalities? And, how can we do so regardless of whether X and Y are of the same, or of different data type, be it discrete, numeric, or mixed? These are exactly the questions we answer. We take an information theoretic approach, based on the Minimum Description Length principle, from which it follows that first describing the data over cause and then that of effect given cause is shorter than the reverse direction. Simply put, if Y can be explained more succinctly by a set of classification or regression trees conditioned on X, than in the opposite direction, we conclude that X causes Y . Empirical evaluation on a wide range of data shows that our method, Crack, infers the correct causal direction reliably and with high accuracy on a wide range of settings, outperforming the state of the art by a wide margin

History

Preferred Citation

Alexander Marx and Jilles Vreeken. Causal Inference on Multivariate and Mixed Type Data. In: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database (ECML PKDD). 2018.

Primary Research Area

  • Empirical and Behavioral Security

Name of Conference

European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database (ECML PKDD)

Legacy Posted Date

2019-06-07

Open Access Type

  • Unknown

BibTeX

@inproceedings{cispa_all_2910, title = "Causal Inference on Multivariate and Mixed Type Data", author = "Marx, Alexander and Vreeken, Jilles", booktitle="{European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database (ECML PKDD)}", year="2018", }

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