Back
Exploring the causal order of binary variables via exponential hierarchies of Markov kernels
We propose a new algorithm for estimating the causal structure that underlies the observed dependence among n (n>=4) binary variables X_1,...,X_n. Our inference principle states that the factorization of the joint probability into conditional probabilities for X_j given X_1,...,X_{j-1} often leads to simpler terms if the order of variables is compatible with the directed acyclic graph representing the causal structure. We study joint measures of OR/AND gates and show that the complexity of the conditional probabilities (the so-called Markov kernels), defined by a hierarchy of exponential models, depends on the order of the variables. Some toy and real-data experiments support our inference rule.
@inproceedings{4456, title = {Exploring the causal order of binary variables via exponential hierarchies of Markov kernels}, journal = {Proceedings of the 15th European Symposium on Artificial Neural Networks (ESANN 2007)}, booktitle = {ESANN 2007}, abstract = {We propose a new algorithm for estimating the causal structure that underlies the observed dependence among n (n>=4) binary variables X_1,...,X_n. Our inference principle states that the factorization of the joint probability into conditional probabilities for X_j given X_1,...,X_{j-1} often leads to simpler terms if the order of variables is compatible with the directed acyclic graph representing the causal structure. We study joint measures of OR/AND gates and show that the complexity of the conditional probabilities (the so-called Markov kernels), defined by a hierarchy of exponential models, depends on the order of the variables. Some toy and real-data experiments support our inference rule.}, pages = {465-470}, publisher = {D-Side}, organization = {Max-Planck-Gesellschaft}, school = {Biologische Kybernetik}, address = {Evere, Belgium}, month = apr, year = {2007}, slug = {4456}, author = {Sun, X. and Janzing, D.}, month_numeric = {4} }