]> 2026-04-04T19:52:37+02:00 Publications:Causal discovery using clusters from observational data 500 en <p>Many methods have been proposed over the years for distinguishing causes from effects using observational data only, and new ones are continuously being developed – deducing causal relationships is difficult enough that we do not hope to ever get the perfect one. Instead, we progress by creating powerful heuristics, capable of capturing more and more of the hints that are present in real data.</p><p>One type of such hints, quite surprisingly rarely explicitly addressed by existing methods, is in-homogeneities in the data. Clusters are a very typical occurrence that should be taken into account, and exploited, in the process of identifying causes and effects. In this paper, we discuss the potential benefits, and explore the hints that clusters in the data can provide for causal discovery. We propose a new method, and show, using both artificial and real data, that accounting for clusters in the data leads to more accurate learning of causal structures.</p> FAIM'18 Workshop on CausalML, Stockholm, Sweden, July 15, 2018 http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:1303420 Conference Paper Causal discovery using clusters from observational data 2018 2019-04-11T20:23:05Z 2458585.3493634 Causal discovery using clusters from observational data 500 0 plainlist list 1 [[:Publications:Causal discovery using clusters from observational data]] Causal discovery using clusters from observational data# QUERY1d840611bacaf052054708250f657435 500 [2,"smw-datavalue-property-restricted-annotation-use","Author"] Causal discovery using clusters from observational data# ERRe24f17e090c98448f3de99d03182b7b6