Abstract
Ancestral graphs (AGs) are graphical causal models that can represent uncertainty about the presence of latent confounders, and can be inferred from data. Here, we present an algorithmic framework for efficiently testing, constructing, and enumerating m-separators in AGs. Moreover, we present a new constructive criterion for covariate adjustment in directed acyclic graphs (DAGs) and maximal ancestral graphs (MAGs) that characterizes adjustment sets as mseparators in a subgraph. Jointly, these results allow to find all adjustment sets that can identify a desired causal effect with multivariate exposures and outcomes in the presence of latent confounding. Our results generalize and improve upon several existing solutions for special cases of these problems.
| Original language | English |
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| Title of host publication | Uncertainty in Artificial Intelligence - Proceedings of the 30th Conference, UAI 2014 |
| Publisher | AUAI Press |
| Pages | 907-916 |
| Number of pages | 10 |
| ISBN (Print) | 9780974903910 |
| Publication status | Published - 2014 |
| Event | 30th Conference on Uncertainty in Artificial Intelligence, UAI 2014 - Quebec City, Canada Duration: 23 Jul 2014 → 27 Jul 2014 |
Conference
| Conference | 30th Conference on Uncertainty in Artificial Intelligence, UAI 2014 |
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| Country/Territory | Canada |
| City | Quebec City |
| Period | 23/07/14 → 27/07/14 |