Error asymmetry in causal and anticausal regression
Error asymmetry in causal and anticausal regression
复制标题
因果回归和反因果回归中的误差不对称
DOI：
10.1007/s41237-017-0022-z
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Shimizu Shohei
中科院分区：
文献类型：
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作者：
Blobaum Patrick;Washio Takashi;Shimizu Shohei
在没有进一步了解数据是如何产生的情况下，通常很难对单变量设置中的预期预测误差做出任何陈述。最近的研究表明，关于数据生成过程的真实的潜在因果结构的知识对各种机器学习设置都有影响。假设一个加性噪声和数据生成机制与其输入之间的独立性，我们得出了一个新的连接之间的内在因果关系的两个变量和预期的预测误差。我们制定的定理，作为预测模型的真实数据生成函数的预期误差一般较小时，从其原因预测的效果，相反，更大时，从其效果预测的原因。该定理意味着取决于预测方向的误差的不对称性。这进一步证实了人工和真实世界数据集的经验评估。
It is generally difficult to make any statements about the expected prediction error in an univariate setting without further knowledge about how the data were generated. Recent work showed that knowledge about the real underlying causal structure of a data generation process has implications for various machine learning settings. Assuming an additive noise and an independence between data generating mechanism and its input, we draw a novel connection between the intrinsic causal relationship of two variables and the expected prediction error. We formulate the theorem that the expected error of the true data generating function as prediction model is generally smaller when the effect is predicted from its cause and, on the contrary, greater when the cause is predicted from its effect. The theorem implies an asymmetry in the error depending on the prediction direction. This is further corroborated with empirical evaluations in artificial and real-world data sets.