Nikolaou, N;
Sechidis, K;
(2020)
Inferring Causal Direction from Observational Data: A Complexity Approach.
In:
Machine Learning for Pharma and Healthcare Applications ECML PKDD 2020 Workshop (PharML 2020).
PharML 2020
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Abstract
At the heart of causal structure learning from observational data lies a deceivingly simple question: given two statistically dependent random variables, which one has a causal effect on the other? This is impossible to answer using statistical dependence testing alone and requires that we make additional assumptions. We propose several fast and simple criteria for distinguishing cause and effect in pairs of discrete or continuous random variables. The intuition behind them is that predicting the effect variable using the cause variable should be ‘simpler’ than the reverse – different notions of ‘simplicity’ giving rise to different criteria. We demonstrate the accuracy of the criteria on synthetic data generated under a broad family of causal mechanisms and types of noise.
| Type: | Proceedings paper |
|---|---|
| Title: | Inferring Causal Direction from Observational Data: A Complexity Approach |
| Event: | PharML 2020 |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://sites.google.com/view/pharml2020/home |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Causal Structure Learning, Causality, Causal Direction, Information Theory, Minimum Description Length, Decision Trees |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10125829 |
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