CauseEffect Inference by Comparing Regression Errors
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Proceedings of the TwentyFirst International Conference on Artificial Intelligence and Statistics, PMLR 84:900909, 2018.
Abstract
We address the problem of inferring the causal relation between two variables by comparing the leastsquares errors of the predictions in both possible causal directions. Under the assumption of an independence between the function relating cause and effect, the conditional noise distribution, and the distribution of the cause, we show that the errors are smaller in causal direction if both variables are equally scaled and the causal relation is close to deterministic. Based on this, we provide an easily applicable method that only requires a regression in both possible causal directions. The performance of this method is compared with different related causal inference methods in various artificial and realworld data sets.
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