(2018) Causal Inference on Multivariate and Mixed Type Data.
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Abstract
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
| Item Type: | Conference or Workshop Item (A Paper) (Paper) |
|---|---|
| Divisions: | Jilles Vreeken (Exploratory Data Analysis) |
| Conference: | ECML PKDD European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database (PKDD and ECML combined from 2008) |
| Depositing User: | Jilles Vreeken |
| Date Deposited: | 07 Jun 2019 06:58 |
| Last Modified: | 10 May 2021 11:37 |
| Primary Research Area: | NRA5: Empirical & Behavioral Security |
| URI: | https://publications.cispa.saarland/id/eprint/2910 |
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