Material Detail

Ancestor Relations in the Presence of Unobserved Variables

Ancestor Relations in the Presence of Unobserved Variables

This video was recorded at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Athens 2011. Bayesian networks (BNs) are an appealing model for causal and noncausal dependencies among a set of variables. Learning BNs from observational data is challenging due to the nonidentifiability of the network structure and model misspecification in the presence of unobserved (latent) variables. Here, we investigate the prospects of Bayesian learning of ancestor relations, including arcs, in the presence and absence of unobserved variables. An exact dynamic programming algorithm to compute the respective posterior probabilities is developed, under the complete data assumption. Our experimental results show that ancestor relations between observed variables, arcs in particular, can be learned with good power even when a majority of the involved variables are unobserved. For comparison, deduction of ancestor relations from single maximum a posteriori network structures or their Markov equivalence class appears somewhat inferior to Bayesian averaging. We also discuss some shortcomings of applying existing conditional independence test based methods for learning ancestor relations.


  • User Rating
  • Comments
  • Learning Exercises
  • Bookmark Collections
  • Course ePortfolios
  • Accessibility Info

More about this material


Log in to participate in the discussions or sign up if you are not already a MERLOT member.