Name
Abstract | ||
|---|---|---|
The use of Bayesian networks for modeling causal systems has achieved widespread recognition with Judea Pearl's Causality (2000). There, Pearl developed a "do-calculus" for reasoning about the effects of deterministic causal interventions on a system. Here we discuss some of the different kinds of intervention that arise when indeterminstic interventions are allowed, generalizing Pearl's account. We also point out the danger of the naive use of Bayesian networks for causal reasoning, which can lead to the mis-estimation of causal effects. We illustrate these ideas with a graphical user interface we have developed for causal modeling. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1007/978-3-540-28633-2_35 | Lecture Notes in Artificial Intelligence |
Keywords | Field | DocType |
bayesian network,causal reasoning,causal models,graphic user interface | Causal reasoning,Causality,Computer science,Generalization,Bayesian network,Artificial intelligence,Deterministic system,Causal system,User interface,Machine learning,Causal model | Conference |
Volume | ISSN | Citations |
3157 | 0302-9743 | 19 |
PageRank | References | Authors |
1.68 | 3 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kevin B. Korb | 1 | 400 | 52.03 |
| Lucas R. Hope | 2 | 47 | 5.09 |
| Ann E. Nicholson | 3 | 692 | 88.01 |
| Karl Axnick | 4 | 20 | 2.38 |