id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_3jstltojlnahjhwxdidtdnycqy	Patrick Blöbaum	Analysis of cause-effect inference by comparing regression errors	2019	29	.pdf	application/pdf	12718	2356	74	inference methods in different artificial and real-world data sets. and effect to a certain function class: For linear relations with non-Gaussian independent assumption, the effect data may contain information about the relation between cause and approach Regression Error based Causal Inference (RECI) and summarize the algorithm the causal direction in various artificially generated and observed real-world data sets. Generally, ANM performs the best in all data sets. performed evaluations with artificial data sets where the input distribution and the noise performing functions and parameters of the different causal inference methods. In case of RECI, Theorem 1 states an equality of the MSE if the functional relation is linear and, thus, the causal direction can not be inferred. While the cause and noise also have a dependency in the SIM-c data sets, the performance Under the assumption of an independence among the data generating function, the	./cache/work_3jstltojlnahjhwxdidtdnycqy.pdf	./txt/work_3jstltojlnahjhwxdidtdnycqy.txt