Peter Spirtes

Publication Activity (10 Years)

Years Active: 1988-2024
Publications (10 Years): 32

Top Topics

Causal Discovery

Latent Variable Models

Causal Relationships

Top Venues

Probabilistic Graphical Models

Publications

Zeyu Tang, Jialu Wang, Yang Liu, Peter Spirtes, Kun Zhang
Procedural Fairness Through Decoupling Objectionable Data Generating Components. ICLR (2024)
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 (2024)
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)
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) (2024)
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)
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 (2024)
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 (2024)
Donghuo Zeng, Roberto Sebastian Legaspi, Yuewen Sun, Xinshuai Dong, Kazushi Ikeda, Peter Spirtes, Kun Zhang
Counterfactual Reasoning Using Predicted Latent Personality Dimensions for Optimizing Persuasion Outcome. PERSUASIVE (2024)
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 (2024)
Negar Kiyavash, Elias Bareinboim, Todd P. Coleman, Alex Dimakis, Bernhard Schlkopf, Peter Spirtes, Kun Zhang, Robert Nowak
Editorial Special Issue on Causality: Fundamental Limits and Applications. IEEE J. Sel. Areas Inf. Theory 4 (2023)
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 (2023)
Zeyu Tang, Jialu Wang, Yang Liu, Peter Spirtes, Kun Zhang
Procedural Fairness Through Decoupling Objectionable Data Generating Components. CoRR (2023)
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. CoRR (2023)
Haoyue Dai, Peter Spirtes, Kun Zhang
Independence Testing-Based Approach to Causal Discovery under Measurement Error and Linear Non-Gaussian Models. CoRR (2022)
Shuyan Wang, Peter Spirtes
A Uniformly Consistent Estimator of non-Gaussian Causal Effects Under the k-Triangle-Faithfulness Assumption. CLeaR (2022)
Bryan Andrews, Gregory F. Cooper, Thomas S. Richardson, Peter Spirtes
The m-connecting imset and factorization for ADMG models. CoRR (2022)
Haoyue Dai, Peter Spirtes, Kun Zhang
Independence Testing-Based Approach to Causal Discovery under Measurement Error and Linear Non-Gaussian Models. NeurIPS (2022)
Shuyan Wang, Peter Spirtes
A Uniformly Consistent Estimator of non-Gaussian Causal Effects Under the k-Triangle-Faithfulness Assumption. CoRR (2021)
Naji Shajarisales, Peter Spirtes, Kun Zhang
Learning from Positive and Unlabeled Data by Identifying the Annotation Process. CoRR (2020)
Daniel Malinsky, Peter Spirtes
Learning the Structure of a Nonstationary Vector Autoregression. AISTATS (2019)
Andrew J. Sedgewick, Kristina Buschur, Ivy Shi, Joseph D. Ramsey, Vineet K. Raghu, Dimitris V. Manatakis, Yingze Zhang, Jessica Bon, Divay Chandra, Chad Karoleski, Frank C. Sciurba, Peter Spirtes, Clark Glymour, Panayiotis V. Benos
Mixed graphical models for integrative causal analysis with application to chronic lung disease diagnosis and prognosis. Bioinform. 35 (7) (2019)
Daniel Malinsky, Peter Spirtes
Causal Structure Learning from Multivariate Time Series in Settings with Unmeasured Confounding. CD@KDD (2018)
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)
Vineet K. Raghu, Joseph D. Ramsey, Alison Morris, Dimitrios V. Manatakis, Peter Spirtes, Panos K. Chrysanthis, Clark Glymour, Panayiotis V. Benos
Comparison of strategies for scalable causal discovery of latent variable models from mixed data. Int. J. Data Sci. Anal. 6 (1) (2018)
Daniel Malinsky, Peter Spirtes
Estimating bounds on causal effects in high-dimensional and possibly confounded systems. Int. J. Approx. Reason. 88 (2017)
Andrew J. Sedgewick, Joseph D. Ramsey, Peter Spirtes, Clark Glymour, Panayiotis V. Benos
Mixed Graphical Models for Causal Analysis of Multi-modal Variables. CoRR (2017)
Kun Zhang, Mingming Gong, Joseph D. Ramsey, Kayhan Batmanghelich, Peter Spirtes, Clark Glymour
Causal Discovery in the Presence of Measurement Error: Identifiability Conditions. CoRR (2017)
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)
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)
Jiji Zhang, Peter Spirtes
The three faces of faithfulness. Synth. 193 (4) (2016)
Juan Miguel Ogarrio, Peter Spirtes, Joe Ramsey
A Hybrid Causal Search Algorithm for Latent Variable Models. Probabilistic Graphical Models (2016)
Daniel Malinsky, Peter Spirtes
Estimating Causal Effects with Ancestral Graph Markov Models. Probabilistic Graphical Models (2016)
Erich Kummerfeld, Joe Ramsey, Renjie Yang, Peter Spirtes, Richard Scheines
Causal Clustering for 2-Factor Measurement Models. ECML/PKDD (2) (2014)
Tianjiao Chu, Richard Scheines, Peter Spirtes
Semi-Instrumental Variables: A Test for Instrument Admissibility CoRR (2013)
Peter Spirtes, Christopher Meek, Thomas S. Richardson
Causal Inference in the Presence of Latent Variables and Selection Bias CoRR (2013)
Doris Entner, Patrik O. Hoyer, Peter Spirtes
Data-driven covariate selection for nonparametric estimation of causal effects. AISTATS (2013)
Peter Spirtes
Calculation of Entailed Rank Constraints in Partially Non-Linear and Cyclic Models. UAI (2013)
Peter Spirtes
Calculation of Entailed Rank Constraints in Partially Non-Linear and Cyclic Models. CoRR (2013)
Peter Spirtes
Detecting Causal Relations in the Presence of Unmeasured Variables CoRR (2013)
Peter Spirtes
Directed Cyclic Graphical Representations of Feedback Models CoRR (2013)
Subramani Mani, Peter Spirtes, Gregory F. Cooper
A theoretical study of Y structures for causal discovery. CoRR (2012)
Jiji Zhang, Peter Spirtes
A Transformational Characterization of Markov Equivalence for Directed Acyclic Graphs with Latent Variables CoRR (2012)
Ricardo Bezerra de Andrade e Silva, Richard Scheines, Clark Glymour, Peter Spirtes
Learning Measurement Models for Unobserved Variables CoRR (2012)
Peter Grunwald, Peter Spirtes
Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence (2010) CoRR (2012)
Ayesha R. Ali, Thomas S. Richardson, Peter Spirtes, Jiji Zhang
Towards Characterizing Markov Equivalence Classes for Directed Acyclic Graphs with Latent Variables CoRR (2012)
Jiji Zhang, Peter Spirtes
Strong Faithfulness and Uniform Consistency in Causal Inference CoRR (2012)
Gustavo Lacerda, Peter Spirtes, Joseph D. Ramsey, Patrik O. Hoyer
Discovering Cyclic Causal Models by Independent Components Analysis CoRR (2012)
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 (2012)
Doris Entner, Patrik O. Hoyer, Peter Spirtes
Statistical test for consistent estimation of causal effects in linear non-Gaussian models. AISTATS (2012)
Joseph D. Ramsey, Jiji Zhang, Peter Spirtes
Adjacency-Faithfulness and Conservative Causal Inference. CoRR (2012)
Joseph D. Ramsey, Peter Spirtes, Clark Glymour
On meta-analyses of imaging data and the mixture of records. NeuroImage 57 (2) (2011)
Jiji Zhang, Peter Spirtes
Intervention, determinism, and the causal minimality condition. Synth. 182 (3) (2011)
Robert E. Tillman, Peter Spirtes
Learning equivalence classes of acyclic models with latent and selection variables from multiple datasets with overlapping variables. AISTATS (2011)
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) (2010)
Peter Spirtes
Introduction to Causal Inference. J. Mach. Learn. Res. 11 (2010)
Robert E. Tillman, Peter Spirtes
When causality matters for prediction. NIPS Causality: Objectives and Assessment (2010)
Robert E. Tillman, Arthur Gretton, Peter Spirtes
Nonlinear directed acyclic structure learning with weakly additive noise models. NIPS (2009)
Gustavo Lacerda, Peter Spirtes, Joseph D. Ramsey, Patrik O. Hoyer
Discovering Cyclic Causal Models by Independent Components Analysis. UAI (2008)
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)
Jiji Zhang, Peter Spirtes
Detection of Unfaithfulness and Robust Causal Inference. Minds Mach. 18 (2) (2008)
Xue Bai, Rema Padman, Joseph D. Ramsey, Peter Spirtes
Tabu Search-Enhanced Graphical Models for Classification in High Dimensions. INFORMS J. Comput. 20 (3) (2008)
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)
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 (2006)
Joseph D. Ramsey, Jiji Zhang, Peter Spirtes
Adjacency-Faithfulness and Conservative Causal Inference. UAI (2006)
Alessio Moneta, Peter Spirtes
Graphical Models for the Identification of Causal Structures in Multivariate Time Series Models. JCIS (2006)
Subramani Mani, Gregory F. Cooper, Peter Spirtes
A Theoretical Study of Y Structures for Causal Discovery. UAI (2006)
Jiji Zhang, Peter Spirtes
A Transformational Characterization of Markov Equivalence for Directed Acyclic Graphs with Latent Variables. UAI (2005)
Ayesha R. Ali, Thomas S. Richardson, Peter Spirtes, Jiji Zhang
Towards Characterizing Markov Equivalence Classes for Directed Acyclic Graphs with Latent Variables. UAI (2005)
Ricardo Bezerra de Andrade e Silva, Richard Scheines, Clark Glymour, Peter Spirtes
Learning Measurement Models for Unobserved Variables. UAI (2003)
Jiji Zhang, Peter Spirtes
Strong Faithfulness and Uniform Consistency in Causal Inference. UAI (2003)
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) (2003)
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) (2002)
Tianjiao Chu, Richard Scheines, Peter Spirtes
Semi-Instrumental Variables: A Test for Instrument Admissibility. UAI (2001)
Peter Spirtes
An Anytime Algorithm for Causal Inference. AISTATS (2001)
Peter Spirtes, Gregory F. Cooper
An experiment in causal discovery using a pneumona database. AISTATS (1999)
Peter Spirtes, Thomas S. Richardson, Christopher Meek
Heuristic Greedy Search Algorithms for Latent Variable Models. AISTATS (1997)
Peter Spirtes, Thomas S. Richardson
A Polynomial Time Algorithm for Determining DAG Equivalence in the Presence of Latent Variables and Selection Bias. AISTATS (1997)
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) (1997)
Thomas S. Richardson, Peter Spirtes, Clark Glymour
A Note on Cyclic Graphs and Dynamical Feedback Systems. AISTATS (1997)
Glenn Shafer, Alexander Kogan, Peter Spirtes
Vanishing TETRAD Differences and Model Structure. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 4 (3) (1996)
Peter Spirtes, Christopher Meek, Thomas S. Richardson
Causal Inference in the Presence of Latent Variables and Selection Bias. UAI (1995)
Peter Spirtes, Christopher Meek
Learning Bayesian Networks with Discrete Variables from Data. KDD (1995)
Peter Spirtes
Directed Cyclic Graphical Representations of Feedback Models. UAI (1995)
Richard Scheines, Peter Spirtes
Finding latent variable models in large databases. Int. J. Intell. Syst. 7 (7) (1992)
Peter Spirtes
Detecting Causal Relations in the Presence of Unmeasured Variables. UAI (1991)
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) (1988)