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Peter Spirtes
Peter L. Spirtes
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- affiliation: Carnegie Mellon University, Pittsburgh, USA
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2020 – today
- 2024
- [j22]Yujia Zheng, Biwei Huang, Wei Chen, Joseph D. Ramsey, Mingming Gong, Ruichu Cai, Shohei Shimizu, Peter Spirtes, Kun Zhang:
Causal-learn: Causal Discovery in Python. J. Mach. Learn. Res. 25: 60:1-60:8 (2024) - [j21]Kun Zhang
, Ilya Shpitser, Sara Magliacane, Davide Bacciu, Fei Wu, Changshui Zhang, Peter Spirtes:
IEEE Transactions on Neural Networks and Learning Systems Special Issue on Causal Discovery and Causality-Inspired Machine Learning. IEEE Trans. Neural Networks Learn. Syst. 35(4): 4899-4901 (2024) - [c41]Haoyue Dai, Ignavier Ng, Gongxu Luo, Peter Spirtes, Petar Stojanov, Kun Zhang:
Gene Regulatory Network Inference in the Presence of Dropouts: a Causal View. ICLR 2024 - [c40]Xinshuai Dong, Biwei Huang, Ignavier Ng, Xiangchen Song, Yujia Zheng, Songyao Jin, Roberto Legaspi, Peter Spirtes, Kun Zhang:
A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables. ICLR 2024 - [c39]Zeyu Tang, Jialu Wang, Yang Liu, Peter Spirtes, Kun Zhang:
Procedural Fairness Through Decoupling Objectionable Data Generating Components. ICLR 2024 - [c38]Ignavier Ng, Xinshuai Dong, Haoyue Dai, Biwei Huang, Peter Spirtes, Kun Zhang:
Score-Based Causal Discovery of Latent Variable Causal Models. ICML 2024 - [c37]Donghuo Zeng
Counterfactual Reasoning Using Predicted Latent Personality Dimensions for Optimizing Persuasion Outcome. PERSUASIVE 2024: 287-300 - [i29]Jingling Li, Zeyu Tang, Xiaoyu Liu, Peter Spirtes, Kun Zhang, Liu Leqi, Yang Liu:
Steering LLMs Towards Unbiased Responses: A Causality-Guided Debiasing Framework. CoRR abs/2403.08743 (2024) - [i28]Haoyue Dai, Ignavier Ng, Gongxu Luo, Peter Spirtes, Petar Stojanov, Kun Zhang:
Gene Regulatory Network Inference in the Presence of Dropouts: a Causal View. CoRR abs/2403.15500 (2024) - [i27]Donghuo Zeng, Roberto Legaspi, Yuewen Sun, Xinshuai Dong, Kazushi Ikeda, Peter Spirtes, Kun Zhang:
Counterfactual Reasoning Using Predicted Latent Personality Dimensions for Optimizing Persuasion Outcome. CoRR abs/2404.13792 (2024) - [i26]Usman Gohar
Long-Term Fairness Inquiries and Pursuits in Machine Learning: A Survey of Notions, Methods, and Challenges. CoRR abs/2406.06736 (2024) - [i25]Xinshuai Dong, Ignavier Ng, Biwei Huang, Yuewen Sun, Songyao Jin, Roberto Legaspi, Peter Spirtes, Kun Zhang:
On the Parameter Identifiability of Partially Observed Linear Causal Models. CoRR abs/2407.16975 (2024) - [i24]Joseph D. Ramsey, Bryan Andrews, Peter Spirtes:
Choosing DAG Models Using Markov and Minimal Edge Count in the Absence of Ground Truth. CoRR abs/2409.20187 (2024) - 2023
- [j20]Negar Kiyavash
Editorial Special Issue on Causality: Fundamental Limits and Applications. IEEE J. Sel. Areas Inf. Theory 4: iv (2023) - [i23]Yujia Zheng, Biwei Huang, Wei Chen, Joseph D. Ramsey, Mingming Gong, Ruichu Cai, Shohei Shimizu
Causal-learn: Causal Discovery in Python. CoRR abs/2307.16405 (2023) - [i22]Zeyu Tang, Jialu Wang, Yang Liu, Peter Spirtes, Kun Zhang:
Procedural Fairness Through Decoupling Objectionable Data Generating Components. CoRR abs/2311.14688 (2023) - [i21]Xinshuai Dong, Biwei Huang, Ignavier Ng, Xiangchen Song, Yujia Zheng, Songyao Jin, Roberto Legaspi, Peter Spirtes, Kun Zhang:
A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables. CoRR abs/2312.11001 (2023) - 2022
- [c36]Shuyan Wang, Peter Spirtes:
A Uniformly Consistent Estimator of non-Gaussian Causal Effects Under the k-Triangle-Faithfulness Assumption. CLeaR 2022: 861-876 - [c35]Haoyue Dai, Peter Spirtes, Kun Zhang:
Independence Testing-Based Approach to Causal Discovery under Measurement Error and Linear Non-Gaussian Models. NeurIPS 2022 - [i20]Bryan Andrews, Gregory F. Cooper, Thomas S. Richardson, Peter Spirtes:
The m-connecting imset and factorization for ADMG models. CoRR abs/2207.08963 (2022) - [i19]Haoyue Dai, Peter Spirtes, Kun Zhang:
Independence Testing-Based Approach to Causal Discovery under Measurement Error and Linear Non-Gaussian Models. CoRR abs/2210.11021 (2022) - 2021
- [i18]Shuyan Wang, Peter Spirtes:
A Uniformly Consistent Estimator of non-Gaussian Causal Effects Under the k-Triangle-Faithfulness Assumption. CoRR abs/2107.01333 (2021) - 2020
- [i17]Naji Shajarisales, Peter Spirtes, Kun Zhang:
Learning from Positive and Unlabeled Data by Identifying the Annotation Process. CoRR abs/2003.01067 (2020)
2010 – 2019
- 2019
- [j19]Andrew J. Sedgewick, Kristina Buschur, Ivy Shi, Joseph D. Ramsey, Vineet K. Raghu, Dimitris V. Manatakis, Yingze Zhang
Mixed graphical models for integrative causal analysis with application to chronic lung disease diagnosis and prognosis. Bioinform. 35(7): 1204-1212 (2019) - [j18]Eric V. Strobl
Estimating and Controlling the False Discovery Rate of the PC Algorithm Using Edge-specific P-Values. ACM Trans. Intell. Syst. Technol. 10(5): 46:1-46:37 (2019) - [c34]Daniel Malinsky, Peter Spirtes:
Learning the Structure of a Nonstationary Vector Autoregression. AISTATS 2019: 2986-2994 - 2018
- [j17]Vineet K. Raghu
Comparison of strategies for scalable causal discovery of latent variable models from mixed data. Int. J. Data Sci. Anal. 6(1): 33-45 (2018) - [j16]Eric V. Strobl
Fast causal inference with non-random missingness by test-wise deletion. Int. J. Data Sci. Anal. 6(1): 47-62 (2018) - [c33]Daniel Malinsky, Peter Spirtes:
Causal Structure Learning from Multivariate Time Series in Settings with Unmeasured Confounding. CD@KDD 2018: 23-47 - [c32]Kun Zhang, Mingming Gong, Joseph D. Ramsey, Kayhan Batmanghelich, Peter Spirtes, Clark Glymour:
Causal Discovery with Linear Non-Gaussian Models under Measurement Error: Structural Identifiability Results. UAI 2018: 1063-1072 - 2017
- [j15]Daniel Malinsky
Estimating bounds on causal effects in high-dimensional and possibly confounded systems. Int. J. Approx. Reason. 88: 371-384 (2017) - [c31]Kai-Wen Liang, Qinsi Wang, Cheryl Telmer, Divyaa Ravichandran, Peter Spirtes, Natasa Miskov-Zivanov:
Methods to Expand Cell Signaling Models Using Automated Reading and Model Checking. CMSB 2017: 145-159 - [c30]Fattaneh Jabbari, Joseph D. Ramsey, Peter Spirtes, Gregory F. Cooper:
Discovery of Causal Models that Contain Latent Variables Through Bayesian Scoring of Independence Constraints. ECML/PKDD (2) 2017: 142-157 - [i16]Andrew J. Sedgewick, Joseph D. Ramsey, Peter Spirtes, Clark Glymour, Panayiotis V. Benos:
Mixed Graphical Models for Causal Analysis of Multi-modal Variables. CoRR abs/1704.02621 (2017) - [i15]Kun Zhang, Mingming Gong, Joseph D. Ramsey, Kayhan Batmanghelich, Peter Spirtes, Clark Glymour:
Causal Discovery in the Presence of Measurement Error: Identifiability Conditions. CoRR abs/1706.03768 (2017) - 2016
- [j14]Jiji Zhang
The three faces of faithfulness. Synth. 193(4): 1011-1027 (2016) - [c29]Daniel Malinsky, Peter Spirtes:
Estimating Causal Effects with Ancestral Graph Markov Models. Probabilistic Graphical Models 2016: 299-309 - [c28]Juan Miguel Ogarrio, Peter Spirtes, Joe Ramsey:
A Hybrid Causal Search Algorithm for Latent Variable Models. Probabilistic Graphical Models 2016: 368-379 - 2014
- [c27]Erich Kummerfeld, Joe Ramsey, Renjie Yang, Peter Spirtes, Richard Scheines:
Causal Clustering for 2-Factor Measurement Models. ECML/PKDD (2) 2014: 34-49 - 2013
- [c26]Doris Entner, Patrik O. Hoyer, Peter Spirtes:
Data-driven covariate selection for nonparametric estimation of causal effects. AISTATS 2013: 256-264 - [c25]Peter Spirtes:
Calculation of Entailed Rank Constraints in Partially Non-Linear and Cyclic Models. UAI 2013 - [i14]Tianjiao Chu, Richard Scheines, Peter Spirtes:
Semi-Instrumental Variables: A Test for Instrument Admissibility. CoRR abs/1301.2261 (2013) - [i13]Peter Spirtes:
Directed Cyclic Graphical Representations of Feedback Models. CoRR abs/1302.4982 (2013) - [i12]Peter Spirtes, Christopher Meek, Thomas S. Richardson:
Causal Inference in the Presence of Latent Variables and Selection Bias. CoRR abs/1302.4983 (2013) - [i11]Peter Spirtes:
Detecting Causal Relations in the Presence of Unmeasured Variables. CoRR abs/1303.5754 (2013) - [i10]Peter Spirtes:
Calculation of Entailed Rank Constraints in Partially Non-Linear and Cyclic Models. CoRR abs/1309.7004 (2013) - 2012
- [c24]Doris Entner, Patrik O. Hoyer, Peter Spirtes:
Statistical test for consistent estimation of causal effects in linear non-Gaussian models. AISTATS 2012: 364-372 - [i9]Peter Grunwald, Peter Spirtes:
Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence (2010). CoRR abs/1205.2597 (2012) - [i8]Patrik O. Hoyer, Aapo Hyvärinen, Richard Scheines, Peter Spirtes, Joseph D. Ramsey, Gustavo Lacerda, Shohei Shimizu:
Causal discovery of linear acyclic models with arbitrary distributions. CoRR abs/1206.3260 (2012) - [i7]Gustavo Lacerda, Peter Spirtes, Joseph D. Ramsey, Patrik O. Hoyer:
Discovering Cyclic Causal Models by Independent Components Analysis. CoRR abs/1206.3273 (2012) - [i6]Joseph D. Ramsey, Jiji Zhang, Peter Spirtes:
Adjacency-Faithfulness and Conservative Causal Inference. CoRR abs/1206.6843 (2012) - [i5]Subramani Mani, Peter Spirtes, Gregory F. Cooper:
A theoretical study of Y structures for causal discovery. CoRR abs/1206.6853 (2012) - [i4]Ayesha R. Ali, Thomas S. Richardson, Peter Spirtes, Jiji Zhang:
Towards Characterizing Markov Equivalence Classes for Directed Acyclic Graphs with Latent Variables. CoRR abs/1207.1365 (2012) - [i3]Jiji Zhang, Peter Spirtes:
A Transformational Characterization of Markov Equivalence for Directed Acyclic Graphs with Latent Variables. CoRR abs/1207.1419 (2012) - [i2]Jiji Zhang, Peter Spirtes:
Strong Faithfulness and Uniform Consistency in Causal Inference. CoRR abs/1212.2506 (2012) - [i1]Ricardo Bezerra de Andrade e Silva, Richard Scheines, Clark Glymour, Peter Spirtes:
Learning Measurement Models for Unobserved Variables. CoRR abs/1212.2516 (2012) - 2011
- [j13]Joseph D. Ramsey, Peter Spirtes, Clark Glymour:
On meta-analyses of imaging data and the mixture of records. NeuroImage 57(2): 323-330 (2011) - [j12]Jiji Zhang
Intervention, determinism, and the causal minimality condition. Synth. 182(3): 335-347 (2011) - [c23]Robert E. Tillman, Peter Spirtes:
Learning equivalence classes of acyclic models with latent and selection variables from multiple datasets with overlapping variables. AISTATS 2011: 3-15 - 2010
- [j11]Peter Spirtes:
Introduction to Causal Inference. J. Mach. Learn. Res. 11: 1643-1662 (2010) - [j10]Clark Glymour, David Danks, Bruce Glymour, Frederick Eberhardt, Joseph D. Ramsey, Richard Scheines, Peter Spirtes, Choh Man Teng, Jiji Zhang
Actual causation: a stone soup essay. Synth. 175(2): 169-192 (2010) - [c22]Robert E. Tillman, Peter Spirtes:
When causality matters for prediction. NIPS Causality: Objectives and Assessment 2010: 137-146 - [e2]Peter Grünwald, Peter Spirtes:
UAI 2010, Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence, Catalina Island, CA, USA, July 8-11, 2010. AUAI Press 2010, ISBN 978-0-9749039-6-5 [contents]
2000 – 2009
- 2009
- [c21]Robert E. Tillman, Arthur Gretton, Peter Spirtes:
Nonlinear directed acyclic structure learning with weakly additive noise models. NIPS 2009: 1847-1855 - 2008
- [j9]Xue Bai, Rema Padman, Joseph D. Ramsey, Peter Spirtes:
Tabu Search-Enhanced Graphical Models for Classification in High Dimensions. INFORMS J. Comput. 20(3): 423-437 (2008) - [j8]Jiji Zhang
Detection of Unfaithfulness and Robust Causal Inference. Minds Mach. 18(2): 239-271 (2008) - [c20]Patrik O. Hoyer, Aapo Hyvärinen, Richard Scheines, Peter Spirtes, Joseph D. Ramsey, Gustavo Lacerda, Shohei Shimizu:
Causal discovery of linear acyclic models with arbitrary distributions. UAI 2008: 282-289 - [c19]Gustavo Lacerda, Peter Spirtes, Joseph D. Ramsey, Patrik O. Hoyer:
Discovering Cyclic Causal Models by Independent Components Analysis. UAI 2008: 366-374 - [c18]Isabelle Guyon, Constantin F. Aliferis, Gregory F. Cooper, André Elisseeff, Jean-Philippe Pellet, Peter Spirtes, Alexander R. Statnikov:
Design and Analysis of the Causation and Prediction Challenge. WCCI Causation and Prediction Challenge 2008: 1-33 - [e1]Isabelle Guyon, Constantin F. Aliferis, Gregory F. Cooper, André Elisseeff, Jean-Philippe Pellet, Peter Spirtes, Alexander R. Statnikov:
Causation and Prediction Challenge at WCCI 2008, Hong Kong, June 1-6, 2008. JMLR Proceedings 3, JMLR.org 2008 [contents] - 2006
- [j7]Ricardo Bezerra de Andrade e Silva, Richard Scheines, Clark Glymour, Peter Spirtes:
Learning the Structure of Linear Latent Variable Models. J. Mach. Learn. Res. 7: 191-246 (2006) - [c17]Alessio Moneta
Graphical Models for the Identification of Causal Structures in Multivariate Time Series Models. JCIS 2006 - [c16]Subramani Mani, Gregory F. Cooper, Peter Spirtes:
A Theoretical Study of Y Structures for Causal Discovery. UAI 2006 - [c15]Joseph D. Ramsey, Jiji Zhang, Peter Spirtes:
Adjacency-Faithfulness and Conservative Causal Inference. UAI 2006 - 2005
- [c14]Ayesha R. Ali, Thomas S. Richardson, Peter Spirtes, Jiji Zhang:
Towards Characterizing Markov Equivalence Classes for Directed Acyclic Graphs with Latent Variables. UAI 2005: 10-17 - [c13]Jiji Zhang, Peter Spirtes:
A Transformational Characterization of Markov Equivalence for Directed Acyclic Graphs with Latent Variables. UAI 2005: 667-674 - 2003
- [j6]Tianjiao Chu, Clark Glymour, Richard Scheines, Peter Spirtes:
A Statistical Problem for Inference to Regulatory Structure from Associations of Gene Expression Measurements with Microarrays. Bioinform. 19(9): 1147-1152 (2003) - [c12]Ricardo Bezerra de Andrade e Silva, Richard Scheines, Clark Glymour, Peter Spirtes:
Learning Measurement Models for Unobserved Variables. UAI 2003: 543-550 - [c11]Jiji Zhang, Peter Spirtes:
Strong Faithfulness and Uniform Consistency in Causal Inference. UAI 2003: 632-639 - 2002
- [j5]Joseph D. Ramsey, Paul Gazis, Ted Roush, Peter Spirtes, Clark Glymour:
Automated Remote Sensing with Near Infrared Reflectance Spectra: Carbonate Recognition. Data Min. Knowl. Discov. 6(3): 277-293 (2002) - 2001
- [c10]Peter Spirtes:
An Anytime Algorithm for Causal Inference. AISTATS 2001: 278-285 - [c9]Tianjiao Chu, Richard Scheines, Peter Spirtes:
Semi-Instrumental Variables: A Test for Instrument Admissibility. UAI 2001: 83-90 - 2000
- [b1]Peter Spirtes, Clark Glymour, Richard Scheines:
Causation, Prediction, and Search, Second Edition. Adaptive computation and machine learning, MIT Press 2000, ISBN 978-0-262-19440-2, pp. I-XXI, 1-543
1990 – 1999
- 1999
- [c8]Peter Spirtes, Gregory F. Cooper:
An experiment in causal discovery using a pneumona database. AISTATS 1999 - 1997
- [j4]Gregory F. Cooper, Constantin F. Aliferis, Richard Ambrosino, John M. Aronis, Bruce G. Buchanan, Rich Caruana, Michael J. Fine, Clark Glymour, Geoffrey J. Gordon, Barbara H. Hanusa, Janine E. Janosky, Christopher Meek, Tom M. Mitchell, Thomas S. Richardson, Peter Spirtes:
An evaluation of machine-learning methods for predicting pneumonia mortality. Artif. Intell. Medicine 9(2): 107-138 (1997) - [c7]Thomas S. Richardson, Peter Spirtes, Clark Glymour:
A Note on Cyclic Graphs and Dynamical Feedback Systems. AISTATS 1997: 421-428 - [c6]Peter Spirtes, Thomas S. Richardson, Christopher Meek:
Heuristic Greedy Search Algorithms for Latent Variable Models. AISTATS 1997: 481-488 - [c5]Peter Spirtes, Thomas S. Richardson:
A Polynomial Time Algorithm for Determining DAG Equivalence in the Presence of Latent Variables and Selection Bias. AISTATS 1997: 489-500 - 1996
- [j3]Glenn Shafer, Alexander Kogan, Peter Spirtes:
Vanishing TETRAD Differences and Model Structure. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 4(3): 209-224 (1996) - 1995
- [c4]Peter Spirtes, Christopher Meek:
Learning Bayesian Networks with Discrete Variables from Data. KDD 1995: 294-299 - [c3]Peter Spirtes:
Directed Cyclic Graphical Representations of Feedback Models. UAI 1995: 491-498 - [c2]Peter Spirtes, Christopher Meek, Thomas S. Richardson:
Causal Inference in the Presence of Latent Variables and Selection Bias. UAI 1995: 499-506 - 1992
- [j2]Richard Scheines, Peter Spirtes:
Finding latent variable models in large databases. Int. J. Intell. Syst. 7(7): 609-621 (1992) - 1991
- [c1]Peter Spirtes:
Detecting Causal Relations in the Presence of Unmeasured Variables. UAI 1991: 392-397
1980 – 1989
- 1988
- [j1]James E. Mogush, Dominique Carrega, Peter Spirtes, Mark S. Fox:
Treatment selection by constraint propagation a case study in cutting fluid selection. Artif. Intell. Eng. Des. Anal. Manuf. 2(3): 135-168 (1988)
Coauthor Index
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