Paper Info
Title
Toward Accounting for Hidden Common Causes When Inferring Cause and Effect from Observational Data
Abstract
Identifying causal relationships from observation data is difficult, in large part, due to the presence of hidden common causes. In some cases, where just the right patterns of conditional independence and dependence lie in the data---for example, Y-structures---it is possible to identify cause and effect. In other cases, the analyst deliberately makes an uncertain assumption that hidden common causes are absent, and infers putative causal relationships to be tested in a randomized trial. Here, we consider a third approach, where there are sufficient clues in the data such that hidden common causes can be inferred.
Year
DOI
Venue
2019
10.1145/3309720
ACM Transactions on Intelligent Systems and Technology (TIST)
Keywords
Field
DocType
Hidden common cause, genomics, linear mixed model
Observational study,Computer science,Artificial intelligence,Machine learning
Journal
Volume
Issue
ISSN
10
5
2157-6904
Citations 
PageRank 
References 
0
0.34
0
Authors
1
Name
Order
Citations
PageRank
David Heckerman169511419.21