Name
Abstract | ||
|---|---|---|
The long-standing identification problem for causal effects in graphical
models has many partial results but lacks a systematic study. We show how
computer algebra can be used to either prove that a causal effect can be
identified, generically identified, or show that the effect is not generically
identifiable. We report on the results of our computations for linear
structural equation models, where we determine precisely which causal effects
are generically identifiable for all graphs on three and four vertices. |
Year | Venue | Keywords |
|---|---|---|
2010 | UAI | graphical model,computer algebra,structural equation model |
Field | DocType | ISSN |
Discrete mathematics,Graph,Structural equation modeling,Algebra,Vertex (geometry),Symbolic computation,Graphical model,Parameter identification problem,Mathematics,Computation | Conference | Proceedings of the 26th Conference of Uncertainty in Artificial
Intelligence (2010) |
Citations | PageRank | References |
4 | 0.58 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luis David García-Puente | 1 | 81 | 10.52 |
| Sarah Spielvogel | 2 | 4 | 0.58 |
| Seth Sullivant | 3 | 93 | 19.17 |