Spirtes, Peter

50 publications

AISTATS 2025 Causal Representation Learning from General Environments Under Nonparametric Mixing Ignavier Ng, Shaoan Xie, Xinshuai Dong, Peter Spirtes, Kun Zhang

ICML 2025 Latent Variable Causal Discovery Under Selection Bias Haoyue Dai, Yiwen Qiu, Ignavier Ng, Xinshuai Dong, Peter Spirtes, Kun Zhang

TMLR 2025 Long-Term Fairness Inquiries and Pursuits in Machine Learning: A Survey of Notions, Methods, and Challenges Usman Gohar, Zeyu Tang, Jialu Wang, Kun Zhang, Peter Spirtes, Yang Liu, Lu Cheng

ICML 2025 Permutation-Based Rank Test in the Presence of Discretization and Application in Causal Discovery with Mixed Data Xinshuai Dong, Ignavier Ng, Boyang Sun, Haoyue Dai, Guang-Yuan Hao, Shunxing Fan, Peter Spirtes, Yumou Qiu, Kun Zhang

ICLR 2025 Prompting Fairness: Integrating Causality to Debias Large Language Models Jingling Li, Zeyu Tang, Xiaoyu Liu, Peter Spirtes, Kun Zhang, Liu Leqi, Yang Liu

ICML 2025 Reflection-Window Decoding: Text Generation with Selective Refinement Zeyu Tang, Zhenhao Chen, Xiangchen Song, Loka Li, Yunlong Deng, Yifan Shen, Guangyi Chen, Peter Spirtes, Kun Zhang

CLeaR 2025 Selecting Accurate Subgraphical Models from Possibly Inaccurate Graphical Models Yi Han, Joseph Ramsey, Peter Spirtes

ICLR 2025 When Selection Meets Intervention: Additional Complexities in Causal Discovery Haoyue Dai, Ignavier Ng, Jianle Sun, Zeyu Tang, Gongxu Luo, Xinshuai Dong, Peter Spirtes, Kun Zhang

ICLR 2024 A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables Xinshuai Dong, Biwei Huang, Ignavier Ng, Xiangchen Song, Yujia Zheng, Songyao Jin, Roberto Legaspi, Peter Spirtes, Kun Zhang

MLOSS 2024 Causal-Learn: Causal Discovery in Python Yujia Zheng, Biwei Huang, Wei Chen, Joseph Ramsey, Mingming Gong, Ruichu Cai, Shohei Shimizu, Peter Spirtes, Kun Zhang

ICLR 2024 Gene Regulatory Network Inference in the Presence of Dropouts: A Causal View Haoyue Dai, Ignavier Ng, Gongxu Luo, Peter Spirtes, Petar Stojanov, Kun Zhang

NeurIPS 2024 Identifying Latent State-Transition Processes for Individualized Reinforcement Learning Yuewen Sun, Biwei Huang, Yu Yao, Donghuo Zeng, Xinshuai Dong, Songyao Jin, Boyang Sun, Roberto Legaspi, Kazushi Ikeda, Peter Spirtes, Kun Zhang

NeurIPS 2024 On the Parameter Identifiability of Partially Observed Linear Causal Models Xinshuai Dong, Ignavier Ng, Biwei Huang, Yuewen Sun, Songyao Jin, Roberto Legaspi, Peter Spirtes, Kun Zhang

ICLR 2024 Procedural Fairness Through Decoupling Objectionable Data Generating Components Zeyu Tang, Jialu Wang, Yang Liu, Peter Spirtes, Kun Zhang

ICML 2024 Score-Based Causal Discovery of Latent Variable Causal Models Ignavier Ng, Xinshuai Dong, Haoyue Dai, Biwei Huang, Peter Spirtes, Kun Zhang

ICLRW 2024 Steering LLMs Towards Unbiased Responses: A Causality-Guided Debiasing Framework Jingling Li, Zeyu Tang, Xiaoyu Liu, Peter Spirtes, Kun Zhang, Liu Leqi, Yang Liu

CLeaR 2022 A Uniformly Consistent Estimator of Non-Gaussian Causal Effects Under the $k$-Triangle-Faithfulness Assumption Shuyan Wang, Peter Spirtes

NeurIPS 2022 Independence Testing-Based Approach to Causal Discovery Under Measurement Error and Linear Non-Gaussian Models Haoyue Dai, Peter Spirtes, Kun Zhang

AISTATS 2020 On the Completeness of Causal Discovery in the Presence of Latent Confounding with Tiered Background Knowledge Bryan Andrews, Peter Spirtes, Gregory F. Cooper

AISTATS 2019 Learning the Structure of a Nonstationary Vector Autoregression Daniel Malinsky, Peter Spirtes

UAI 2018 Causal Discovery with Linear Non-Gaussian Models Under Measurement Error: Structural Identifiability Results Kun Zhang, Mingming Gong, Joseph D. Ramsey, Kayhan Batmanghelich, Peter Spirtes, Clark Glymour

ECML-PKDD 2017 Discovery of Causal Models That Contain Latent Variables Through Bayesian Scoring of Independence Constraints Fattaneh Jabbari, Joseph D. Ramsey, Peter Spirtes, Gregory F. Cooper

PGM 2016 A Hybrid Causal Search Algorithm for Latent Variable Models Juan Miguel Ogarrio, Peter Spirtes, Joe Ramsey

PGM 2016 Estimating Causal Effects with Ancestral Graph Markov Models Daniel Malinsky, Peter Spirtes

ECML-PKDD 2014 Causal Clustering for 2-Factor Measurement Models Erich Kummerfeld, Joe Ramsey, Renjie Yang, Peter Spirtes, Richard Scheines

UAI 2013 Calculation of Entailed Rank Constraints in Partially Non-Linear and Cyclic Models Peter Spirtes

AISTATS 2013 Data-Driven Covariate Selection for Nonparametric Estimation of Causal Effects Doris Entner, Patrik O. Hoyer, Peter Spirtes

AISTATS 2012 Statistical Test for Consistent Estimation of Causal Effects in Linear Non-Gaussian Models Doris Entner, Patrik Hoyer, Peter Spirtes

AISTATS 2011 Learning Equivalence Classes of Acyclic Models with Latent and Selection Variables from Multiple Datasets with Overlapping Variables Robert Tillman, Peter Spirtes

JMLR 2010 Introduction to Causal Inference Peter Spirtes

UAI 2010 UAI 2010, Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence, Catalina Island, CA, USA, July 8-11, 2010 Peter Grünwald, Peter Spirtes

NeurIPS 2009 Nonlinear Directed Acyclic Structure Learning with Weakly Additive Noise Models Arthur Gretton, Peter Spirtes, Robert E. Tillman

UAI 2008 Causal Discovery of Linear Acyclic Models with Arbitrary Distributions Patrik O. Hoyer, Aapo Hyvärinen, Richard Scheines, Peter Spirtes, Joseph D. Ramsey, Gustavo Lacerda, Shohei Shimizu

UAI 2008 Discovering Cyclic Causal Models by Independent Components Analysis Gustavo Lacerda, Peter Spirtes, Joseph D. Ramsey, Patrik O. Hoyer

UAI 2006 A Theoretical Study of Y Structures for Causal Discovery Subramani Mani, Gregory F. Cooper, Peter Spirtes

UAI 2006 Adjacency-Faithfulness and Conservative Causal Inference Joseph D. Ramsey, Jiji Zhang, Peter Spirtes

JMLR 2006 Learning the Structure of Linear Latent Variable Models Ricardo Silva, Richard Scheine, Clark Glymour, Peter Spirtes

UAI 2005 A Transformational Characterization of Markov Equivalence for Directed Acyclic Graphs with Latent Variables Jiji Zhang, Peter Spirtes

UAI 2005 Towards Characterizing Markov Equivalence Classes for Directed Acyclic Graphs with Latent Variables Ayesha R. Ali, Thomas S. Richardson, Peter Spirtes, Jiji Zhang

UAI 2003 Learning Measurement Models for Unobserved Variables Ricardo Bezerra de Andrade e Silva, Richard Scheines, Clark Glymour, Peter Spirtes

UAI 2003 Strong Faithfulness and Uniform Consistency in Causal Inference Jiji Zhang, Peter Spirtes

AISTATS 2001 An Anytime Algorithm for Causal Inference Peter Spirtes

UAI 2001 Semi-Instrumental Variables: A Test for Instrument Admissibility Tianjiao Chu, Richard Scheines, Peter Spirtes

AISTATS 1999 An Experiment in Causal Discovery Using a Pneumonia Database Peter Spirtes, Gregory F. Cooper

AISTATS 1997 A Note on Cyclic Graphs and Dynamical Feedback Systems Thomas S. Richardson, Peter Spirtes, Clark Glymour

AISTATS 1997 A Polynomial Time Algorithm for Determining DAG Equivalence in the Presence of Latent Variables and Selection Bias Peter Spirtes, Thomas S. Richardson

AISTATS 1997 Heuristic Greedy Search Algorithms for Latent Variable Models Peter Spirtes, Thomas S.Richardson, Christopher Meek

AISTATS 1995 A Generalization of the Tetrad Representation Theorem Glenn Shafer, Alexander Kogan, Peter Spirtes

UAI 1995 Causal Inference in the Presence of Latent Variables and Selection Bias Peter Spirtes, Christopher Meek, Thomas S. Richardson

UAI 1995 Directed Cyclic Graphical Representations of Feedback Models Peter Spirtes