Header logo is

Causal Inference by Choosing Graphs with Most Plausible Markov Kernels

2006

Conference Paper

ei


We propose a new inference rule for estimating causal structure that underlies the observed statistical dependencies among n random variables. Our method is based on comparing the conditional distributions of variables given their direct causes (the so-called Markov kernels") for all hypothetical causal directions and choosing the most plausible one. We consider those Markov kernels most plausible, which maximize the (conditional) entropies constrained by their observed first moment (expectation) and second moments (variance and covariance with its direct causes) based on their given domain. In this paper, we discuss our inference rule for causal relationships between two variables in detail, apply it to a real-world temperature data set with known causality and show that our method provides a correct result for the example.

Author(s): Sun, X. and Janzing, D. and Schölkopf, B.
Book Title: Proceedings of the 9th International Symposium on Artificial Intelligence and Mathematics
Journal: Proceedings of the Ninth International Symposium on Artificial Intelligence and Mathematics (AI & Math 2006
Pages: 1-11
Year: 2006
Month: January
Day: 0

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Event Name: ISAIM 2006
Event Place: Fort Lauderdale, FL, USA

Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF
Web

BibTex

@inproceedings{5635,
  title = {Causal Inference by Choosing Graphs with Most Plausible Markov Kernels},
  author = {Sun, X. and Janzing, D. and Sch{\"o}lkopf, B.},
  journal = {Proceedings of the Ninth International Symposium on Artificial Intelligence and Mathematics (AI & Math 2006},
  booktitle = {Proceedings of the 9th International Symposium on Artificial Intelligence and Mathematics},
  pages = {1-11},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = jan,
  year = {2006},
  month_numeric = {1}
}