Peter Spirtes

Orcid: 0000-0002-1385-190X

Affiliations:
  • Carnegie Mellon University, Pittsburgh, USA


According to our database1, Peter Spirtes authored at least 73 papers between 1988 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

On csauthors.net:

Bibliography

2024
IEEE Transactions on Neural Networks and Learning Systems Special Issue on Causal Discovery and Causality-Inspired Machine Learning.
IEEE Trans. Neural Networks Learn. Syst., April, 2024

Causal-learn: Causal Discovery in Python.
J. Mach. Learn. Res., 2024

Choosing DAG Models Using Markov and Minimal Edge Count in the Absence of Ground Truth.
CoRR, 2024

On the Parameter Identifiability of Partially Observed Linear Causal Models.
CoRR, 2024

Long-Term Fairness Inquiries and Pursuits in Machine Learning: A Survey of Notions, Methods, and Challenges.
CoRR, 2024

Steering LLMs Towards Unbiased Responses: A Causality-Guided Debiasing Framework.
CoRR, 2024

Counterfactual Reasoning Using Predicted Latent Personality Dimensions for Optimizing Persuasion Outcome.
Proceedings of the Persuasive Technology - 19th International Conference, 2024

Score-Based Causal Discovery of Latent Variable Causal Models.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Procedural Fairness Through Decoupling Objectionable Data Generating Components.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Gene Regulatory Network Inference in the Presence of Dropouts: a Causal View.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023
Editorial Special Issue on Causality: Fundamental Limits and Applications.
IEEE J. Sel. Areas Inf. Theory, 2023

2022
The m-connecting imset and factorization for ADMG models.
CoRR, 2022

Independence Testing-Based Approach to Causal Discovery under Measurement Error and Linear Non-Gaussian Models.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

A Uniformly Consistent Estimator of non-Gaussian Causal Effects Under the k-Triangle-Faithfulness Assumption.
Proceedings of the 1st Conference on Causal Learning and Reasoning, 2022

2020
Learning from Positive and Unlabeled Data by Identifying the Annotation Process.
CoRR, 2020

2019
Estimating and Controlling the False Discovery Rate of the PC Algorithm Using Edge-specific P-Values.
ACM Trans. Intell. Syst. Technol., 2019

Mixed graphical models for integrative causal analysis with application to chronic lung disease diagnosis and prognosis.
Bioinform., 2019

Learning the Structure of a Nonstationary Vector Autoregression.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018
Fast causal inference with non-random missingness by test-wise deletion.
Int. J. Data Sci. Anal., 2018

Comparison of strategies for scalable causal discovery of latent variable models from mixed data.
Int. J. Data Sci. Anal., 2018

Causal Discovery with Linear Non-Gaussian Models under Measurement Error: Structural Identifiability Results.
Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018

Causal Structure Learning from Multivariate Time Series in Settings with Unmeasured Confounding.
Proceedings of 2018 ACM SIGKDD Workshop on Causal Discovery, 2018

2017
Estimating bounds on causal effects in high-dimensional and possibly confounded systems.
Int. J. Approx. Reason., 2017

Causal Discovery in the Presence of Measurement Error: Identifiability Conditions.
CoRR, 2017

Mixed Graphical Models for Causal Analysis of Multi-modal Variables.
CoRR, 2017

Discovery of Causal Models that Contain Latent Variables Through Bayesian Scoring of Independence Constraints.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2017

Methods to Expand Cell Signaling Models Using Automated Reading and Model Checking.
Proceedings of the Computational Methods in Systems Biology, 2017

2016
The three faces of faithfulness.
Synth., 2016

A Hybrid Causal Search Algorithm for Latent Variable Models.
Proceedings of the Probabilistic Graphical Models - Eighth International Conference, 2016

Estimating Causal Effects with Ancestral Graph Markov Models.
Proceedings of the Probabilistic Graphical Models - Eighth International Conference, 2016

2014
Causal Clustering for 2-Factor Measurement Models.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2014

2013
Calculation of Entailed Rank Constraints in Partially Non-Linear and Cyclic Models.
Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence, 2013

Data-driven covariate selection for nonparametric estimation of causal effects.
Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, 2013

2012
Statistical test for consistent estimation of causal effects in linear non-Gaussian models.
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

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

2011
Intervention, determinism, and the causal minimality condition.
Synth., 2011

On meta-analyses of imaging data and the mixture of records.
NeuroImage, 2011

Learning equivalence classes of acyclic models with latent and selection variables from multiple datasets with overlapping variables.
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011

2010
Actual causation: a stone soup essay.
Synth., 2010

When causality matters for prediction.
Proceedings of the Causality: Objectives and Assessment (NIPS 2008 Workshop), 2010

Introduction to Causal Inference.
J. Mach. Learn. Res., 2010

2009
Nonlinear directed acyclic structure learning with weakly additive noise models.
Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

2008
Detection of Unfaithfulness and Robust Causal Inference.
Minds Mach., 2008

Design and Analysis of the Causation and Prediction Challenge.
Proceedings of the Causation and Prediction Challenge at WCCI 2008, 2008

Tabu Search-Enhanced Graphical Models for Classification in High Dimensions.
INFORMS J. Comput., 2008

Discovering Cyclic Causal Models by Independent Components Analysis.
Proceedings of the UAI 2008, 2008

Causal discovery of linear acyclic models with arbitrary distributions.
Proceedings of the UAI 2008, 2008

2006
Learning the Structure of Linear Latent Variable Models.
J. Mach. Learn. Res., 2006

Adjacency-Faithfulness and Conservative Causal Inference.
Proceedings of the UAI '06, 2006

A Theoretical Study of Y Structures for Causal Discovery.
Proceedings of the UAI '06, 2006

Graphical Models for the Identification of Causal Structures in Multivariate Time Series Models.
Proceedings of the 2006 Joint Conference on Information Sciences, 2006

2005
A Transformational Characterization of Markov Equivalence for Directed Acyclic Graphs with Latent Variables.
Proceedings of the UAI '05, 2005

Towards Characterizing Markov Equivalence Classes for Directed Acyclic Graphs with Latent Variables.
Proceedings of the UAI '05, 2005

2003
A Statistical Problem for Inference to Regulatory Structure from Associations of Gene Expression Measurements with Microarrays.
Bioinform., 2003

Strong Faithfulness and Uniform Consistency in Causal Inference.
Proceedings of the UAI '03, 2003

Learning Measurement Models for Unobserved Variables.
Proceedings of the UAI '03, 2003

2002
Automated Remote Sensing with Near Infrared Reflectance Spectra: Carbonate Recognition.
Data Min. Knowl. Discov., 2002

2001
Semi-Instrumental Variables: A Test for Instrument Admissibility.
Proceedings of the UAI '01: Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence, 2001

An Anytime Algorithm for Causal Inference.
Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, 2001

2000
Causation, Prediction, and Search, Second Edition.
Adaptive computation and machine learning, MIT Press, ISBN: 978-0-262-19440-2, 2000

1999
An experiment in causal discovery using a pneumona database.
Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, 1999

1997
An evaluation of machine-learning methods for predicting pneumonia mortality.
Artif. Intell. Medicine, 1997

Heuristic Greedy Search Algorithms for Latent Variable Models.
Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics, 1997

A Polynomial Time Algorithm for Determining DAG Equivalence in the Presence of Latent Variables and Selection Bias.
Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics, 1997

A Note on Cyclic Graphs and Dynamical Feedback Systems.
Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics, 1997

1996
Vanishing TETRAD Differences and Model Structure.
Int. J. Uncertain. Fuzziness Knowl. Based Syst., 1996

1995
Causal Inference in the Presence of Latent Variables and Selection Bias.
Proceedings of the UAI '95: Proceedings of the Eleventh Annual Conference on Uncertainty in Artificial Intelligence, 1995

Directed Cyclic Graphical Representations of Feedback Models.
Proceedings of the UAI '95: Proceedings of the Eleventh Annual Conference on Uncertainty in Artificial Intelligence, 1995

Learning Bayesian Networks with Discrete Variables from Data.
Proceedings of the First International Conference on Knowledge Discovery and Data Mining (KDD-95), 1995

1992
Finding latent variable models in large databases.
Int. J. Intell. Syst., 1992

1991
Detecting Causal Relations in the Presence of Unmeasured Variables.
Proceedings of the UAI '91: Proceedings of the Seventh Annual Conference on Uncertainty in Artificial Intelligence, 1991

1988
Treatment selection by constraint propagation a case study in cutting fluid selection.
Artif. Intell. Eng. Des. Anal. Manuf., 1988


  Loading...