Reference Causal feature learning: An overview [chalupka_causal_2017]

@article{chalupka_causal_2017,
 abstract = {Causal feature learning ({CFL}) (Chalupka et al., Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence. {AUAI} Press, Edinburgh, pp 181–190, 2015) is a causal inference framework rooted in the language of causal graphical models (Pearl J, Reasoning and inference. Cambridge University Press, Cambridge, 2009; Spirtes et al., Causation, Prediction, and Search. Massachusetts Institute of Technology, Massachusetts, 2000), and computational mechanics (Shalizi, {PhD} thesis, University of Wisconsin at Madison, 2001). {CFL} is aimed at discovering high-level causal relations from low-level data, and at reducing the experimental effort to understand confounding among the high-level variables. We first review the scientific motivation for {CFL}, then present a detailed introduction to the framework, laying out the definitions and algorithmic steps. A simple example illustrates the techniques involved in the learning steps and provides visual intuition. Finally, we discuss the limitations of the current framework and list a number of open problems.},
 author = {Chalupka, Krzysztof and Eberhardt, Frederick and Perona, Pietro},
 date = {2017-01},
 doi = {10.1007/s41237-016-0008-2},
 file = {mscChalupka2017_Article_CausalFeatureLearningAnOvervie.pdf:/home/eigil/Dropbox/Matematik/Tekster/mscChalupka2017_Article_CausalFeatureLearningAnOvervie.pdf:application/pdf},
 issn = {0385-7417, 1349-6964},
 journaltitle = {Behaviormetrika},
 langid = {english},
 number = {1},
 pages = {137--164},
 shortjournal = {Behaviormetrika},
 shorttitle = {Causal feature learning},
 title = {Causal feature learning: an overview},
 url = {http://link.springer.com/10.1007/s41237-016-0008-2},
 urldate = {2021-04-04},
 volume = {44}
}