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Proceedings of Machine Learning Research|December 6, 2019
Learning the Structure of a Nonstationary Vector AutoregressionDaniel Malinsky, Peter SpirtesInternational Journal of Approximate Reasoning : Official Publication of the North American Fuzzy Information Processing Society|August 22, 2025
Corrigendum to "Estimating bounds on causal effects in high-dimensional and possibly confounded systems" [Int. J. Approx. Reason. 88 (2017) 371-384]Daniel Malinsky, Peter SpirtesJMLR Workshop and Conference Proceedings|February 21, 2017
Estimating Causal Effects with Ancestral Graph Markov ModelsDaniel Malinsky, Peter SpirtesInternational Journal of Approximate Reasoning : Official Publication of the North American Fuzzy Information Processing Society|December 6, 2017
Estimating bounds on causal effects in high-dimensional and possibly confounded systemsDaniel Malinsky, Peter SpirtesApplied Informatics|May 20, 2016
Causal discovery and inference: concepts and recent methodological advancesPeter Spirtes, Kun ZhangFrontiers in Genetics|June 20, 2019
Review of Causal Discovery Methods Based on Graphical ModelsClark Glymour, Kun Zhang, Peter SpirtesJMLR Workshop and Conference Proceedings|February 28, 2017
A Hybrid Causal Search Algorithm for Latent Variable ModelsJuan Miguel Ogarrio, Peter Spirtes, Joe RamseyProceedings of Machine Learning Research|December 31, 2019
A Potential Outcomes Calculus for Identifying Conditional Path-Specific EffectsDaniel Malinsky, Ilya Shpitser, Thomas RichardsonProceedings of Machine Learning Research|December 31, 2019
Learning Optimal Fair PoliciesRazieh Nabi, Daniel Malinsky, Ilya ShpitserUncertainty in Artificial Intelligence : Proceedings of the ... Conference. Conference on Uncertainty in Artificial Intelligence|December 31, 2019
Causal Inference Under Interference And Network UncertaintyRohit Bhattacharya, Daniel Malinsky, Ilya ShpitserPageof 4