Treatment selection by constraint propagation a case study in cutting fluid selection. - James E. Mogush; Dominique Carrega; Peter Spirtes; Mark S. Fox (Artif. Intell. Eng. Des. Anal. Manuf., 1988)

Detecting Causal Relations in the Presence of Unmeasured Variables. - Peter Spirtes (UAI, 1991)

Finding latent variable models in large databases. - Richard Scheines; Peter Spirtes (Int. J. Intell. Syst., 1992)

Learning Bayesian Networks with Discrete Variables from Data. - Peter Spirtes; Christopher Meek (KDD, 1995)

Directed Cyclic Graphical Representations of Feedback Models. - Peter Spirtes (UAI, 1995)

Causal Inference in the Presence of Latent Variables and Selection Bias. - Peter Spirtes; Christopher Meek; Thomas S. Richardson 001 (UAI, 1995)

Vanishing TETRAD Differences and Model Structure. - Glenn Shafer; Alexander Kogan; Peter Spirtes (Int. J. Uncertain. Fuzziness Knowl. Based Syst., 1996)

A Note on Cyclic Graphs and Dynamical Feedback Systems. - Thomas S. Richardson 001; 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 001 (AISTATS, 1997)

Heuristic Greedy Search Algorithms for Latent Variable Models. - Peter Spirtes; Thomas S. Richardson 001; Christopher Meek (AISTATS, 1997)

An evaluation of machine-learning methods for predicting pneumonia mortality. - 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; et al. (Artif. Intell. Medicine, 1997)

An experiment in causal discovery using a pneumona database. - Peter Spirtes; Gregory F. Cooper (AISTATS, 1999)

Causation, Prediction, and Search, Second Edition. - Peter Spirtes; Clark Glymour; Richard Scheines (MIT Press, 2000)

An Anytime Algorithm for Causal Inference. - Peter Spirtes (AISTATS, 2001)

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

Automated Remote Sensing with Near Infrared Reflectance Spectra: Carbonate Recognition. - Joseph D. Ramsey; Paul Gazis; Ted Roush; Peter Spirtes; Clark Glymour (Data Min. Knowl. Discov., 2002)

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 (UAI, 2003)

A Statistical Problem for Inference to Regulatory Structure from Associations of Gene Expression Measurements with Microarrays. - Tianjiao Chu; Clark Glymour; Richard Scheines; Peter Spirtes (Bioinform., 2003)

Towards Characterizing Markov Equivalence Classes for Directed Acyclic Graphs with Latent Variables. - Ayesha R. Ali; Thomas S. Richardson 001; Peter Spirtes; Jiji Zhang (UAI, 2005)

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

Graphical Models for the Identification of Causal Structures in Multivariate Time Series Models. - Alessio Moneta; Peter Spirtes (JCIS, 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 (UAI, 2006)

Learning the Structure of Linear Latent Variable Models. - Ricardo Bezerra de Andrade e Silva; Richard Scheines; Clark Glymour; Peter Spirtes (J. Mach. Learn. Res., 2006)

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, 2008)

Tabu Search-Enhanced Graphical Models for Classification in High Dimensions. - Xue Bai; Rema Padman; Joseph D. Ramsey; Peter Spirtes (INFORMS J. Comput., 2008)

Design and Analysis of the Causation and Prediction Challenge. - Isabelle Guyon; Constantin F. Aliferis; Gregory F. Cooper; André Elisseeff; Jean-Philippe Pellet; Peter Spirtes; Alexander R. Statnikov (WCCI Causation and Prediction Challenge, 2008)

Detection of Unfaithfulness and Robust Causal Inference. - Jiji Zhang; Peter Spirtes (Minds Mach., 2008)

Nonlinear directed acyclic structure learning with weakly additive noise models. - Robert E. Tillman; Arthur Gretton; Peter Spirtes (NIPS, 2009)

Introduction to Causal Inference. - Peter Spirtes (J. Mach. Learn. Res., 2010)

When causality matters for prediction. - Robert E. Tillman; Peter Spirtes (NIPS Causality: Objectives and Assessment, 2010)

Actual causation: a stone soup essay. - Clark Glymour; David Danks; Bruce Glymour; Frederick Eberhardt; Joseph D. Ramsey; Richard Scheines; Peter Spirtes; Choh Man Teng 001; Jiji Zhang (Synth., 2010)

Learning equivalence classes of acyclic models with latent and selection variables from multiple datasets with overlapping variables. - Robert E. Tillman; Peter Spirtes (AISTATS, 2011)

On meta-analyses of imaging data and the mixture of records. - Joseph D. Ramsey; Peter Spirtes; Clark Glymour (NeuroImage, 2011)

Intervention, determinism, and the causal minimality condition. - Jiji Zhang; Peter Spirtes (Synth., 2011)

Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence (2010) - Peter Grunwald; Peter Spirtes (CoRR, 2012)

Statistical test for consistent estimation of causal effects in linear non-Gaussian models. - Doris Entner; Patrik O. Hoyer; Peter Spirtes (AISTATS, 2012)

Data-driven covariate selection for nonparametric estimation of causal effects. - Doris Entner; Patrik O. Hoyer; Peter Spirtes (AISTATS, 2013)

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

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

Estimating Causal Effects with Ancestral Graph Markov Models. - Daniel Malinsky; Peter Spirtes (Probabilistic Graphical Models, 2016)

A Hybrid Causal Search Algorithm for Latent Variable Models. - Juan Miguel Ogarrio; Peter Spirtes; Joe Ramsey (Probabilistic Graphical Models, 2016)

The three faces of faithfulness. - Jiji Zhang; Peter Spirtes (Synth., 2016)

Methods to Expand Cell Signaling Models Using Automated Reading and Model Checking. - Kai-Wen Liang; Qinsi Wang; Cheryl Telmer; Divyaa Ravichandran; Peter Spirtes; Natasa Miskov-Zivanov (CMSB, 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 (ECML/PKDD (2), 2017)

Mixed Graphical Models for Causal Analysis of Multi-modal Variables. - Andrew J. Sedgewick; Joseph D. Ramsey; Peter Spirtes; Clark Glymour; Panayiotis V. Benos (CoRR, 2017)

Causal Discovery in the Presence of Measurement Error: Identifiability Conditions. - Kun Zhang 001; Mingming Gong; Joseph D. Ramsey; Kayhan Batmanghelich; Peter Spirtes; Clark Glymour (CoRR, 2017)

Estimating bounds on causal effects in high-dimensional and possibly confounded systems. - Daniel Malinsky; Peter Spirtes (Int. J. Approx. Reason., 2017)

Causal Structure Learning from Multivariate Time Series in Settings with Unmeasured Confounding. - Daniel Malinsky; Peter Spirtes (CD@KDD, 2018)

Causal Discovery with Linear Non-Gaussian Models under Measurement Error: Structural Identifiability Results. - Kun Zhang 001; Mingming Gong; Joseph D. Ramsey; Kayhan Batmanghelich; Peter Spirtes; Clark Glymour (UAI, 2018)

Comparison of strategies for scalable causal discovery of latent variable models from mixed data. - Vineet K. Raghu; Joseph D. Ramsey; Alison Morris; Dimitrios V. Manatakis; Peter Spirtes; Panos K. Chrysanthis; Clark Glymour; Panayiotis V. Benos (Int. J. Data Sci. Anal., 2018)

Fast causal inference with non-random missingness by test-wise deletion. - Eric V. Strobl; Shyam Visweswaran; Peter L. Spirtes (Int. J. Data Sci. Anal., 2018)

Learning the Structure of a Nonstationary Vector Autoregression. - Daniel Malinsky; Peter Spirtes (AISTATS, 2019)

Mixed graphical models for integrative causal analysis with application to chronic lung disease diagnosis and prognosis. - 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; et al. (Bioinform., 2019)

Estimating and Controlling the False Discovery Rate of the PC Algorithm Using Edge-specific P-Values. - Eric V. Strobl; Peter L. Spirtes; Shyam Visweswaran (ACM Trans. Intell. Syst. Technol., 2019)

Learning from Positive and Unlabeled Data by Identifying the Annotation Process. - Naji Shajarisales; Peter Spirtes; Kun Zhang 001 (CoRR, 2020)

A Uniformly Consistent Estimator of non-Gaussian Causal Effects Under the k-Triangle-Faithfulness Assumption. - Shuyan Wang; Peter Spirtes (CLeaR, 2022)

Independence Testing-Based Approach to Causal Discovery under Measurement Error and Linear Non-Gaussian Models. - Haoyue Dai; Peter Spirtes; Kun Zhang 001 (NeurIPS, 2022)

The m-connecting imset and factorization for ADMG models. - Bryan Andrews; Gregory F. Cooper; Thomas S. Richardson 001; Peter Spirtes (CoRR, 2022)

Editorial Special Issue on Causality: Fundamental Limits and Applications. - Negar Kiyavash; Elias Bareinboim; Todd P. Coleman; Alex Dimakis; Bernhard Schlkopf; Peter Spirtes; Kun Zhang 001; Robert Nowak 001 (IEEE J. Sel. Areas Inf. Theory, 2023)

Gene Regulatory Network Inference in the Presence of Dropouts: a Causal View. - Haoyue Dai; Ignavier Ng; Gongxu Luo; Peter Spirtes; Petar Stojanov; Kun Zhang 001 (ICLR, 2024)

A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables. - Xinshuai Dong; Biwei Huang; Ignavier Ng; Xiangchen Song; Yujia Zheng 001; Songyao Jin; Roberto Legaspi; Peter Spirtes; Kun Zhang 001 (ICLR, 2024)

Procedural Fairness Through Decoupling Objectionable Data Generating Components. - Zeyu Tang 002; Jialu Wang; Yang Liu 018; Peter Spirtes; Kun Zhang 001 (ICLR, 2024)

Score-Based Causal Discovery of Latent Variable Causal Models. - Ignavier Ng; Xinshuai Dong; Haoyue Dai; Biwei Huang; Peter Spirtes; Kun Zhang 001 (ICML, 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 001 (NeurIPS, 2024)

Identifying Latent State-Transition Processes for Individualized Reinforcement Learning. - Yuewen Sun; Biwei Huang; Yu Yao 005; Donghuo Zeng; Xinshuai Dong; Songyao Jin; Boyang Sun; Roberto Legaspi; Kazushi Ikeda; Peter Spirtes; Kun Zhang 001 (NeurIPS, 2024)

Counterfactual Reasoning Using Predicted Latent Personality Dimensions for Optimizing Persuasion Outcome. - Donghuo Zeng; Roberto Sebastian Legaspi; Yuewen Sun; Xinshuai Dong; Kazushi Ikeda; Peter Spirtes; Kun Zhang 001 (PERSUASIVE, 2024)

Steering LLMs Towards Unbiased Responses: A Causality-Guided Debiasing Framework. - Jingling Li; Zeyu Tang 002; Xiaoyu Liu; Peter Spirtes; Kun Zhang 001; Liu Leqi; Yang Liu 018 (CoRR, 2024)

Choosing DAG Models Using Markov and Minimal Edge Count in the Absence of Ground Truth. - Joseph D. Ramsey; Bryan Andrews; Peter Spirtes (CoRR, 2024)

Causal-learn: Causal Discovery in Python. - Yujia Zheng 001; Biwei Huang; Wei Chen 103; Joseph D. Ramsey; Mingming Gong; Ruichu Cai; Shohei Shimizu; Peter Spirtes; Kun Zhang 001 (J. Mach. Learn. Res., 2024)

IEEE Transactions on Neural Networks and Learning Systems Special Issue on Causal Discovery and Causality-Inspired Machine Learning. - Kun Zhang 001; Ilya Shpitser; Sara Magliacane; Davide Bacciu; Fei Wu 001; Changshui Zhang; Peter Spirtes (IEEE Trans. Neural Networks Learn. Syst., 2024)

Causal Representation Learning from General Environments under Nonparametric Mixing. - Ignavier Ng; Shaoan Xie; Xinshuai Dong; Peter Spirtes; Kun Zhang 001 (AISTATS, 2025)

Selecting Accurate Subgraphical Models from Possibly Inaccurate Graphical Models. - Yi Han; Joseph D. Ramsey; Peter Spirtes (CLeaR, 2025)

When Selection Meets Intervention: Additional Complexities in Causal Discovery. - Haoyue Dai; Ignavier Ng; Jianle Sun; Zeyu Tang 002; Gongxu Luo; Xinshuai Dong; Peter Spirtes; Kun Zhang 001 (ICLR, 2025)

Prompting Fairness: Integrating Causality to Debias Large Language Models. - Jingling Li; Zeyu Tang 002; Xiaoyu Liu; Peter Spirtes; Kun Zhang 001; Liu Leqi; Yang Liu 018 (ICLR, 2025)

Generative Framework for Personalized Persuasion: Inferring Causal, Counterfactual, and Latent Knowledge. - Donghuo Zeng; Roberto Legaspi; Yuewen Sun; Xinshuai Dong; Kazushi Ikeda; Peter Spirtes; Kun Zhang 001 (UMAP, 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 Spirtes (Int. J. Approx. Reason., 2025)

Long-Term Fairness Inquiries and Pursuits in Machine Learning: A Survey of Notions, Methods, and Challenges. - Usman Gohar; Zeyu Tang 002; Jialu Wang; Kun Zhang 001; Peter Spirtes; Yang Liu 018; Lu Cheng 001 (Trans. Mach. Learn. Res., 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 001 (CoRR, 2025)

Reflection-Window Decoding: Text Generation with Selective Refinement. - Zeyu Tang 002; Zhenhao Chen; Loka Li; Xiangchen Song; Yunlong Deng; Yifan Shen 004; Guangyi Chen 002; Peter Spirtes; Kun Zhang 001 (CoRR, 2025)

Causal Discovery and Counterfactual Reasoning to Optimize Persuasive Dialogue Policies. - Donghuo Zeng; Roberto Legaspi; Yuewen Sun; Xinshuai Dong; Kazushi Ikeda; Peter Spirtes; Kun Zhang 001 (CoRR, 2025)