Name
Abstract | ||
|---|---|---|
Heckerman (1993) defined causal independence in terms of a set of temporal conditional independence statements. These statements formalized certain types of causal interaction where (1) the effect is independent of the order that causes are introduced and (2) the impact of a single cause on the effect does not depend on what other causes have previously been applied. In this paper, we introduce art equivalent a temporal characterization of causal independence based on a functional representation of the relationship between causes and the effect. In this representation, the interaction between causes and effect can be written as a nested decomposition of functions. Causal independence can be exploited by representing this decomposition in the belief network, resulting in representations that are more efficient for inference than general causal models. We present empirical results showing the benefits of a causal-independence representation for belief-network inference. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/B978-1-55860-332-5.50041-9 | UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence |
Keywords | DocType | Volume |
temporal conditional independence statement,causal interaction,general causal model,functional representation,causal-independence representation,nested decomposition,belief-network inference,art equivalent,causal independence,temporal characterization,new look | Journal | abs/1302.6814 |
ISBN | Citations | PageRank |
1-55860-332-8 | 33 | 4.37 |
References | Authors | |
4 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| David Heckerman | 1 | 6951 | 1419.21 |
| John S. Breese | 2 | 2475 | 369.52 |