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Detecting low-complexity unobserved causes


Conference Paper


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: PDF


  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},
  month_numeric = {7}