Cause-Effect Inference by Comparing Regression Errors
Cause-Effect Inference by Comparing Regression Errors
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发表时间：
2018-03
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通讯作者：
Patrick Blöbaum;D. Janzing;T. Washio;Shohei Shimizu;B. Scholkopf
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作者：
Patrick Blöbaum;D. Janzing;T. Washio;Shohei Shimizu;B. Scholkopf
我们通过比较两个可能的因果方向上的预测的最小二乘误差来解决推断两个变量之间的因果关系的问题。在因果相关函数、条件噪声分布和原因分布之间独立的假设下，我们表明，如果两个变量等尺度且因果关系接近确定性，则因果方向上的误差较小。基于此，我们提供了一种易于应用的方法，只需要在两个可能的因果方向上进行回归。该方法的性能在各种人工和现实数据集中与不同的相关因果推理方法进行了比较。
We address the problem of inferring the causal relation between two variables by comparing the least-squares 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 real-world data sets.