Abstract
This cause-effect pairs challenge was motivated by the contrast between the costs of performing controlled experiments in order to determine causality and the abundance of observational data. Our goal was to provide a value representing our confidence of causality determined by the observation data which would help identify the most promising variables for experimental verification of their causal relationship. By identifying patterns in functions that generate relevant features, a feature extraction pipeline was architected to allow for the creation of large amounts of complex features with minimal human intervention. Using this pipeline, we were able to finish second in the public leaderboard and first in the private leaderboard. Furthermore, this process by default generates over 20,000 features. In this paper, we analyze which aspects are most important, and create a new pipeline that gets comparable performance with only 324 features.
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Notes
- 1.
Available at https://github.com/diogo149/autocause.
- 2.
See the configs subdirectory of https://github.com/diogo149/CauseEffectPairsPaper.
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Acknowledgment
Special thanks to the organizers of the ChaLearn Cause-Effect Pair Challenge hosted by Kaggle.
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Appendix
Appendix
1.1 Results
See Tables 10.6, 10.7, 10.8, 10.9, 10.10, 10.11, 10.12, and 10.13.
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Almeida, D.M.d. (2019). Pattern-Based Causal Feature Extraction. In: Guyon, I., Statnikov, A., Batu, B. (eds) Cause Effect Pairs in Machine Learning. The Springer Series on Challenges in Machine Learning. Springer, Cham. https://doi.org/10.1007/978-3-030-21810-2_10
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