Detecting low-complexity unobserved causes
2011
Conference Paper
ei
We describe a method that infers whether statistical dependences between two observed variables X and Y are due to a \direct" causal link or only due to a connecting causal path that contains an unobserved variable of low complexity, e.g., a binary variable. This problem is motivated by statistical genetics. Given a genetic marker that is correlated with a phenotype of interest, we want to detect whether this marker is causal or it only correlates with a causal one. Our method is based on the analysis of the location of the conditional distributions P(Y jx) in the simplex of all distributions of Y . We report encouraging results on semi-empirical data.
Author(s): | Janzing, D. and Sgouritsa, E. and Stegle, O. and Peters, J. and Schölkopf, B. |
Pages: | 383-391 |
Year: | 2011 |
Month: | July |
Day: | 0 |
Editors: | FG Cozman and A Pfeffer |
Publisher: | AUAI Press |
Department(s): | Empirical Inference |
Bibtex Type: | Conference Paper (inproceedings) |
Event Name: | 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011) |
Event Place: | Barcelona, Spain |
Address: | Corvallis, OR, USA |
Digital: | 0 |
ISBN: | 978-0-9749039-7-2 |
Links: |
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BibTex @inproceedings{JanzingSSPS2011, title = {Detecting low-complexity unobserved causes}, author = {Janzing, D. and Sgouritsa, E. and Stegle, O. and Peters, J. and Sch{\"o}lkopf, B.}, pages = {383-391}, editors = {FG Cozman and A Pfeffer}, publisher = {AUAI Press}, address = {Corvallis, OR, USA}, month = jul, year = {2011}, doi = {}, month_numeric = {7} } |