Interpreting and using CPDAGs with background knowledge

Emilija Perkovic, Markus Kalisch, Marloes H. Maathuis
Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence, PMLR R15:461-470, 2017.

Abstract

We develop terminology and methods for working with maximally oriented partially directed acyclic graphs (maximal PDAGs). Maximal PDAGs arise from imposing restric- tions on a Markov equivalence class of directed acyclic graphs, or equivalently on its graph- ical representation as a completed partially directed acyclic graph (CPDAG), for exam- ple when adding background knowledge about certain edge orientations. Although maximal PDAGs often arise in practice, causal meth- ods have been mostly developed for CPDAGs. In this paper, we extend such methodology to maximal PDAGs. In particular, we de- velop methodology to read off possible ances- tral relationships, we introduce a graphical cri- terion for covariate adjustment to estimate total causal effects, and we adapt the IDA and joint- IDA frameworks to estimate multi-sets of pos- sible causal effects. We also present a simula- tion study that illustrates the gain in identifia- bility of total causal effects as the background knowledge increases. All methods are imple- mented in the R package pcalg.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR15-perkovic17a, title = {Interpreting and using CPDAGs with background knowledge}, author = {Perkovic, Emilija and Kalisch, Markus and Maathuis, Marloes H.}, booktitle = {Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence}, pages = {461--470}, year = {2017}, editor = {Elidan, Gal and Kersting, Kristian}, volume = {R15}, series = {Proceedings of Machine Learning Research}, month = {11--15 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r15/main/assets/perkovic17a/perkovic17a.pdf}, url = {https://proceedings.mlr.press/r15/perkovic17a.html}, abstract = {We develop terminology and methods for working with maximally oriented partially directed acyclic graphs (maximal PDAGs). Maximal PDAGs arise from imposing restric- tions on a Markov equivalence class of directed acyclic graphs, or equivalently on its graph- ical representation as a completed partially directed acyclic graph (CPDAG), for exam- ple when adding background knowledge about certain edge orientations. Although maximal PDAGs often arise in practice, causal meth- ods have been mostly developed for CPDAGs. In this paper, we extend such methodology to maximal PDAGs. In particular, we de- velop methodology to read off possible ances- tral relationships, we introduce a graphical cri- terion for covariate adjustment to estimate total causal effects, and we adapt the IDA and joint- IDA frameworks to estimate multi-sets of pos- sible causal effects. We also present a simula- tion study that illustrates the gain in identifia- bility of total causal effects as the background knowledge increases. All methods are imple- mented in the R package pcalg.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Interpreting and using CPDAGs with background knowledge %A Emilija Perkovic %A Markus Kalisch %A Marloes H. Maathuis %B Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2017 %E Gal Elidan %E Kristian Kersting %F pmlr-vR15-perkovic17a %I PMLR %P 461--470 %U https://proceedings.mlr.press/r15/perkovic17a.html %V R15 %X We develop terminology and methods for working with maximally oriented partially directed acyclic graphs (maximal PDAGs). Maximal PDAGs arise from imposing restric- tions on a Markov equivalence class of directed acyclic graphs, or equivalently on its graph- ical representation as a completed partially directed acyclic graph (CPDAG), for exam- ple when adding background knowledge about certain edge orientations. Although maximal PDAGs often arise in practice, causal meth- ods have been mostly developed for CPDAGs. In this paper, we extend such methodology to maximal PDAGs. In particular, we de- velop methodology to read off possible ances- tral relationships, we introduce a graphical cri- terion for covariate adjustment to estimate total causal effects, and we adapt the IDA and joint- IDA frameworks to estimate multi-sets of pos- sible causal effects. We also present a simula- tion study that illustrates the gain in identifia- bility of total causal effects as the background knowledge increases. All methods are imple- mented in the R package pcalg. %Z Reissued by PMLR on 04 October 2026.
APA
Perkovic, E., Kalisch, M. & Maathuis, M.H.. (2017). Interpreting and using CPDAGs with background knowledge. Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R15:461-470 Available from https://proceedings.mlr.press/r15/perkovic17a.html. Reissued by PMLR on 04 October 2026.

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