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A new causal discovery heuristic

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Abstract

Probabilistic methods for causal discovery are based on the detection of patterns of correlation between variables. They are based on statistical theory and have revolutionised the study of causality. However, when correlation itself is unreliable, so are probabilistic methods: unusual data can lead to spurious causal links, while nonmonotonic functional relationships between variables can prevent the detection of causal links. We describe a new heuristic method for inferring causality between two continuous variables, based on randomness and unimodality tests and making few assumptions about the data. We evaluate the method against probabilistic and additive noise algorithms on real and artificial datasets, and show that it performs competitively.

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Acknowledgements

Our research was aided by the availability of benchmarks in the UCI Machine Learning Repository [19] and the Cause Effect Pairs collection of [21]. This work was supported in part by Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289.

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Authors and Affiliations

  1. Insight Centre for Data Analytics, Department of Computer Science, University College Cork, Cork, Ireland

    S. D. Prestwich

  2. Department of Management, Cankaya University, Ankara, Turkey

    S. A. Tarim

  3. Department of Economics, Hacettepe University, Ankara, Turkey

    I. Ozkan

Authors
  1. S. D. Prestwich
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  2. S. A. Tarim
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  3. I. Ozkan
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Corresponding author

Correspondence to S. D. Prestwich.

Additional information

Supported in part by a research grant from Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289.

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Prestwich, S.D., Tarim, S.A. & Ozkan, I. A new causal discovery heuristic. Ann Math Artif Intell 82, 245–259 (2018). https://doi.org/10.1007/s10472-018-9575-0

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  • DOI: https://doi.org/10.1007/s10472-018-9575-0

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