Identifying Causal Effects with Computer Algebra

Seth Sullivant, Luis David Garcia-Puente, Sarah Spielvogel
Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:571-578, 2010.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR8-sullivant10a, title = {Identifying Causal Effects with Computer Algebra}, author = {Sullivant, Seth and Garcia-Puente, Luis David and Spielvogel, Sarah}, booktitle = {Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence}, pages = {571--578}, year = {2010}, editor = {Grünwald, Peter and Spirtes, Peter}, volume = {R8}, series = {Proceedings of Machine Learning Research}, month = {08--11 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r8/main/assets/sullivant10a/sullivant10a.pdf}, url = {https://proceedings.mlr.press/r8/sullivant10a.html}, 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.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Identifying Causal Effects with Computer Algebra %A Seth Sullivant %A Luis David Garcia-Puente %A Sarah Spielvogel %B Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2010 %E Peter Grünwald %E Peter Spirtes %F pmlr-vR8-sullivant10a %I PMLR %P 571--578 %U https://proceedings.mlr.press/r8/sullivant10a.html %V R8 %X 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. %Z Reissued by PMLR on 04 October 2026.
APA
Sullivant, S., Garcia-Puente, L.D. & Spielvogel, S.. (2010). Identifying Causal Effects with Computer Algebra. Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R8:571-578 Available from https://proceedings.mlr.press/r8/sullivant10a.html. Reissued by PMLR on 04 October 2026.

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