P. Spirtes

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Causation, prediction, and search

P. Spirtes, C. Glymour, R. Scheines, 1993 .

Discovering Causal Structure.

Kevin T. Kelly, P. Spirtes, C. Glymour, 1989 .

An Algorithm for Fast Recovery of Sparse Causal Graphs

P. Spirtes, C. Glymour, 1991 .

Causality From Probability

P. Spirtes, C. Glymour, R. Scheines, 1989 .

Causation, Prediction, and Search, 2nd Edition

P. Spirtes, C. Glymour, R. Scheines, 2001 .

A Polynomial Time Algorithm For Determining DAG Equivalence in the Presence of Latent Variables and Selection Bias

P. Spirtes, 1996 .

Causal Inference of Ambiguous Manipulations

P. Spirtes, R. Scheines, 2004, Philosophy of Science.

Causation, Prediction, and Search, Second Edition

Richard Scheines, Clark Glymour, Peter Spirtes, 2000, Adaptive computation and machine learning.

Ancestral graph Markov models

P. Spirtes, T. Richardson, 2002 .

A uniformly consistent estimator of causal effects under the k-Triangle-Faithfulness assumption

P. Spirtes, Jiji Zhang, 2014, 1502.00829.

MARKOV EQUIVALENCE FOR ANCESTRAL GRAPHS

P. Spirtes, T. Richardson, R. A. Ali, 2009, 0908.3605.

Learning equivalence classes of acyclic models with latent and selection variables from multiple datasets with overlapping variables

Peter Spirtes, Robert E. Tillman, P. Spirtes, 2011, AISTATS.

Learning Bayesian Networks with Discrete Variables from Data

Christopher Meek, Peter Spirtes, P. Spirtes, 1995, KDD.

Using Path Diagrams as a Structural Equation Modeling Tool

P. Spirtes, C. Glymour, R. Scheines, 1998 .

Latent variables, causal models and overidentifying constraints

P. Spirtes, C. Glymour, 1988 .

Causal structure among measured variables preserved with unmeasured variables

P. Spirtes, C. Glymour, 1990 .

From probability to causality

P. Spirtes, C. Glymour, R. Scheines, 1991 .

Graph-Based Search Procedure for Vector Autoregressive Models

P. Spirtes, Alessio Moneta, 2005 .

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

Richard Scheines, Clark Glymour, Peter Spirtes, 2003, Bioinform..

Uniform consistency in causal inference

J. Robins, P. Spirtes, R. Scheines, 2003 .

Graphical models, causal inference, and econometric models

P. Spirtes, 2005 .

Causal discovery and inference: concepts and recent methodological advances

Peter Spirtes, Kun Zhang, P. Spirtes, 2016, Applied Informatics.

Design and Analysis of the Causation and Prediction Challenge

Isabelle Guyon, Constantin F. Aliferis, Gregory F. Cooper, 2008, WCCI Causation and Prediction Challenge.

Introduction to Causal Inference

Peter Spirtes, P. Spirtes, 2010, J. Mach. Learn. Res..

Review of Causal Discovery Methods Based on Graphical Models

P. Spirtes, C. Glymour, Kun Zhang, 2019, Front. Genet..

Simulation Studies of the Reliability of Computer-Aided Model Specification Using the TETRAD II, EQS, and LISREL Programs

Richard Scheines, Clark Glymour, Peter Spirtes, 1990 .

Inference, Intervention, and Prediction

P. Spirtes, C. Glymour, 1994 .

Prediction and Experimental Design with Graphical Causal Models

P. Spirtes, C. Glymour, R. Scheines, 1992 .

Common Cause In Causal Inference

P. Spirtes, 2011 .

Discovering Cyclic and Acyclic Causal Models by Independent Components Analysis

P. Spirtes, Gustavo Lacerda, J. Ramsey, 2008 .

Data mining tasks and methods: Probabilistic and casual networks: methodology for probabilistic networks

P. Spirtes, 2002 .

Latent Structure and Causal Variables

P. Spirtes, 2001 .

Reply to Comments.

P. Spirtes, C. Glymour, T. Richardson, 1998, Multivariate behavioral research.

Reply to Humphreys and Freedman's Review of Causation, Prediction, and Search

P. Spirtes, C. Glymour, R. Scheines, 1997, The British Journal for the Philosophy of Science.

Causal & Non-Causal Feature Selection for Ridge Regression

P. Spirtes, A. Statnikov, 2009 .

Learning from Positive and Unlabeled Data by Identifying the Annotation Process pdf

Kun Zhang, Peter Spirtes, Naji Shajarisales, 2020, ArXiv.

The dimensionality of mixed ancestral graphs

Christopher Meek, Thomas S. Richardson, Peter Spirtes, 1997 .

Using D-Separation to Calculate Zero Partial Correlations in Linear Models with Correlated Errors

Richard Scheines, Christopher Meek, Thomas S. Richardson, 1996 .

Causal Inference in the Presence of Latent Variables and Selection Bias

Thomas S. Richardson, Christopher Meek, Peter Spirtes, 1995, UAI.

Ancestral Graph Markov Models 1

P. Spirtes, T. Richardson, 2002 .

Building causal graphs from statistical data in the presence of latent variables

Peter Spirtes, P. Spirtes, 1995 .

Conditional Independence in Directed Cyclic Graphical Models for Feedback

Peter Spirtes, P. Spirtes, 1994 .

Chapter 24 Causal Inference

P. Spirtes, C. Glymour, R. Scheines, 2004 .

Learning causality and causality-related learning: some recent progress.

Bernhard Schölkopf, Clark Glymour, Kun Zhang, 2018, National science review.

Actual Causation : A Stone Soup Essay Draft of February 9 , 2007 Authors so far , if they are willing

P. Spirtes, C. Glymour, R. Scheines, 2007 .

Analysis of Microarray Data for Treated Fat Cells

P. Spirtes, C. Glymour, R. Scheines, 2003 .

Evidence of cross-hybridization artifact in expressed sequence tags (ESTs) on cDNA microarrays ⁄

P. Spirtes, C. Glymour, R. Scheines, 2002 .

Truth is among the best explanations: Finding causal explanations of conditional independence and dependence

Richard Scheines, Christopher Meek, Clark Glymour, 1999 .

Uncertainty in artificial intelligence : proceedings of the Twenty-sixth Conference (2010) : July 8-11, 2010, Avalon, CA

Peter Grünwald, Peter Spirtes, Jeff Bilmes, 2010 .

Confounding Equivalence in Causal Inference

P. Spirtes, 2022 .

On Measurement Bias in Causal Inference

P. Spirtes, 2022 .

Equivalence of causal models with latent variables

Peter Spirtes, Thomas Verma, P. Spirtes, 1992 .

Finding Pure Submodels for Improved Differentiation of Bifactor and Second-Order Models

P. Spirtes, R. Scheines, S. Reise, 2017, Structural equation modeling : a multidisciplinary journal.

[Bayesian Analysis in Expert Systems]: Comment: Conditional Independence and Causal Inference

P. Spirtes, C. Glymour, 1993 .

Discovering Causal Relations Among Latent Variables in Directed Acyclical Graphical Models

P. Spirtes, 1996 .

TETRAD II: Tools for Discovery

Richard Scheines, Christopher Meek, Clark Glymour, 1994 .

Independence Testing-Based Approach to Causal Discovery under Measurement Error and Linear Non-Gaussian Models

P. Spirtes, Kun Zhang, Haoyue Dai, 2022, Neural Information Processing Systems.

The three faces of faithfulness

P. Spirtes, Jiji Zhang, 2016, Synthese.

Causal Discovery for Observational Sciences Using Supervised Machine Learning

P. Spirtes, C. Ekstrøm, J. Ramsey, 2022, Journal of Data Science.

Actual causation: a stone soup essay

Choh Man Teng, Frederick Eberhardt, Richard Scheines, 2009, Synthese.

Reply to comment

P. Spirtes, C. Glymour, Christopher Meek, 1995 .

Inferring causation from time series in Earth system sciences

Bernhard Schölkopf, Marten Scheffer, Markus Reichstein, 2019, Nature Communications.

Adjacency-Faithfulness and Conservative Causal Inference pdf

Jiji Zhang, Joseph Ramsey, Peter Spirtes, 2006, UAI.

The TETRAD Project: Constraint Based Aids to Causal Model Specification.

R Scheines, P Spirtes, C Glymour, 1998, Multivariate behavioral research.

Discovering Cyclic Causal Models by Independent Components Analysis pdf

Patrik O. Hoyer, Joseph Ramsey, Peter Spirtes, 2008, UAI.

Directed Cyclic Graphs, Conditional Independence, and Non-Recursive Linear Structural Equation Models

Peter Spirtes, P. Spirtes, 2005 .

Directed Cyclic Graphs, Conditional Independence, and Non-Recursive Linear Constructive Equation Models

Peter Spirtes, P. Spirtes, 1993 .

Calculation of Entailed Rank Constraints in Partially Non-Linear and Cyclic Models pdf

Peter Spirtes, P. Spirtes, 2013, UAI.

On meta-analyses of imaging data and the mixture of records

Clark Glymour, Joseph Ramsey, Peter Spirtes, 2011, NeuroImage.

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

Clark Glymour, Dimitris V. Manatakis, Joseph Ramsey, 2018, Bioinform..

Comparison of strategies for scalable causal discovery of latent variable models from mixed data

Panos K. Chrysanthis, Dimitrios V. Manatakis, P. Spirtes, 2018, International Journal of Data Science and Analytics.

Automated discovery of linear feedback models

Thomas S. Richardson, Peter Spirtes, P. Spirtes, 1996 .

Directed Cyclic Graphical Representations of Feedback Models

Peter Spirtes, P. Spirtes, 1995, UAI.

Conditional Independence in Directed Cyclical Graphical Models Representing Feedback or Mixtures

Peter Spirtes, P. Spirtes, 1994 .

Detecting Causal Relations in the Presence of Unmeasured Variables pdf

Peter Spirtes, P. Spirtes, 1991, UAI.

An evaluation of machine-learning methods for predicting pneumonia mortality

Constantin F. Aliferis, Tom M. Mitchell, Rich Caruana, 1997, Artif. Intell. Medicine.

Evidence of systematic expressed sequence tag IMAGE clone cross-hybridization on cDNA microarrays.

Larry Wasserman, Richard Scheines, Clark Glymour, 2004, Genomics.

Statistical test for consistent estimation of causal effects in linear non-Gaussian models

Patrik O. Hoyer, Peter Spirtes, Doris Entner, 2012, AISTATS.

Learning the Structure of a Nonstationary Vector Autoregression

Daniel Malinsky, Peter Spirtes, P. Spirtes, 2019, AISTATS.

Inferring causation from time series in Earth system sciences

P. Spirtes, C. Glymour, B. Schölkopf, 2019, Nature Communications.

Strong Faithfulness and Uniform Consistency in Causal Inference pdf

Jiji Zhang, Peter Spirtes, P. Spirtes, 2002, UAI.

Causal Discovery with Linear Non-Gaussian Models under Measurement Error: Structural Identifiability Results

Kun Zhang, Mingming Gong, Clark Glymour, 2018, UAI.

Discovering Causal Structure: Artificial Intelligence, Philosophy of Science, and Statistical Modeling

Richard Scheines, Kevin T. Kelly, Clark Glymour, 1987 .

Exploring Causal Structure with the TETRAD Program

Richard Scheines, Clark Glymour, Peter Spirtes, 1988 .

Automated Search for Causal Relations: Theory and Practice

Richard Scheines, Clark Glymour, Peter Spirtes, 2010 .

Finding latent variable models in large databases

Richard Scheines, Peter Spirtes, P. Spirtes, 1992, Int. J. Intell. Syst..

Causal inference

P. Spirtes, P. Spirtes, C. Glymour, 1991, Erkenntnis.

Development of Intelligent, Computerized Aids for the Specification of Casual Models from Large Data Bases

Richard Scheines, Clark Glymour, Peter Spirtes, 1992 .

A qualitative approach to causal modeling

Richard Scheines, Clark Glymour, Peter Spirtes, 1991 .

Intervention, determinism, and the causal minimality condition

Jiji Zhang, Peter Spirtes, P. Spirtes, 2011, Synthese.

Detection of Unfaithfulness and Robust Causal Inference

Jiji Zhang, Peter Spirtes, P. Spirtes, 2008, Minds and Machines.

Data-driven covariate selection for nonparametric estimation of causal effects

Patrik O. Hoyer, Peter Spirtes, Doris Entner, 2013, AISTATS.

Fast causal inference with non-random missingness by test-wise deletion

Shyam Visweswaran, Eric V. Strobl, Peter L. Spirtes, 2017, International Journal of Data Science and Analytics.

Estimating Causal Effects with Ancestral Graph Markov Models

Daniel Malinsky, Peter Spirtes, P. Spirtes, 2016, Probabilistic Graphical Models.

A Theoretical Study of Y Structures for Causal Discovery pdf

Gregory F. Cooper, Peter Spirtes, Subramani Mani, 2006, UAI.

Learning the Structure of Linear Latent Variable Models

Richard Scheines, Clark Glymour, Ricardo Bezerra de Andrade e Silva, 2006, J. Mach. Learn. Res..

Tabu Search-Enhanced Graphical Models for Classification in High Dimensions

Rema Padman, Joseph Ramsey, Xue Bai, 2008, INFORMS J. Comput..

Causal Structure Learning from Time Series Causal Structure Learning from Multivariate Time Series in Settings with Unmeasured Confounding

Daniel Malinsky, Peter Spirtes, P. Spirtes, 2018 .

TETRAD II : tools for causal modeling

Richard Scheines, Christopher Meek, Clark Glymour, 1994 .

Learning Measurement Models for Unobserved Variables

Richard Scheines, Clark Glymour, Ricardo Bezerra de Andrade e Silva, 2002, UAI.

Automated Remote Sensing with Near Infrared Reflectance Spectra: Carbonate Recognition

Clark Glymour, Joseph Ramsey, Peter Spirtes, 2002, Data Mining and Knowledge Discovery.

In Place of Regression

P. Spirtes, C. Glymour, R. Scheines, 1994 .

Discovery of Causal Models that Contain Latent Variables Through Bayesian Scoring of Independence Constraints

Fattaneh Jabbari, Gregory F. Cooper, Joseph Ramsey, 2017, ECML/PKDD.

An Anytime Algorithm for Causal Inference

Peter Spirtes, P. Spirtes, 2001, AISTATS.

A Hybrid Causal Search Algorithm for Latent Variable Models

Peter Spirtes, Juan Miguel Ogarrio, Joe Ramsey, 2016, Probabilistic Graphical Models.

Treatment as Heuristic Construction: A Case Study in Cutting Fluid Selection.

Mark S. Fox, Peter Spirtes, James E. Mogush, 1986 .

A Characterization of Markov Equivalence Classes for Ancestral Graphical Models

Peter Spirtes, P. Spirtes, Jiji Zhang, 2005 .

An introduction to Causal Inference

P. Spirtes, R. Scheines, 1996 .

Tetrad II: User's Manual

Richard Scheines, Christopher Meek, Clark Glymour, 1994 .

The expected complexity of problem solving

Kevin T. Kelly, Clark Glymour, Peter Spirtes, 1987 .

Causal Discovery in the Presence of Measurement Error: Identifiability Conditions pdf

Mingming Gong, Clark Glymour, Kun Zhang, 2017, ArXiv.

Nonlinear directed acyclic structure learning with weakly additive noise models

Arthur Gretton, Peter Spirtes, Robert E. Tillman, 2009, NIPS.

Causal discovery of linear acyclic models with arbitrary distributions

Aapo Hyvärinen, Patrik O. Hoyer, Richard Scheines, 2008, UAI.

A Transformational Characterization of Markov Equivalence for Directed Acyclic Graphs with Latent Variables

Jiji Zhang, Peter Spirtes, P. Spirtes, 2005, UAI.

Estimating and Controlling the False Discovery Rate of the PC Algorithm Using Edge-specific P-Values

Shyam Visweswaran, Eric V. Strobl, Peter L. Spirtes, 2016, ACM Trans. Intell. Syst. Technol..

Comparison of strategies for scalable causal discovery of latent variable models from mixed data

Panos K. Chrysanthis, Clark Glymour, Joseph Ramsey, 2018, International Journal of Data Science and Analytics.

Discovery Algorithms for Causally Sufficient Structures

Richard Scheines, Clark Glymour, Peter Spirtes, 1993 .

Estimating bounds on causal effects in high-dimensional and possibly confounded systems

Daniel Malinsky, Peter Spirtes, P. Spirtes, 2017, Int. J. Approx. Reason..

The m-connecting imset and factorization for ADMG models

Gregory F. Cooper, P. Spirtes, T. Richardson, 2022, ArXiv.

Heuristic greedy search algorithms for latent variable models

Thomas S. Richardson, Christopher Meek, Peter Spirtes, 1997 .

Inferring causation from time series in Earth system sciences

P. Spirtes, C. Glymour, J. Peters, 2019, Nature Communications.

Discussion of Causal Diagrams for Empirical Research by J. Pearl

Stephen E. Fienberg, Clark Glymour, Peter Spirtes, 1995 .

The TETRAD Project: Constraint Based Aids to Causal Model Specification.

P. Spirtes, C. Glymour, R. Scheines, 1998, Multivariate behavioral research.

A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables

P. Spirtes, Biwei Huang, Ignavier Ng, 2023, ArXiv.

Artificial Intelligence in Medicine

Geoffrey J. Gordon, Tom Michael Mitchell, P J Drew, 2018, European Archives of Medical Research.

The Limits of Causal Inference from Observational Data

P. Spirtes, 2007 .

Artificial intelligence in medicine

Geoffrey J. Gordon, Johanna D. Moore, Tom Michael Mitchell, 1989, Springer US.

A Fast Algorithm for Discovering Sparse Causal Graphs

Clark Glymour, Peter Spirtes, P. Spirtes, 1991 .

Artificial intelligence in medicine

Diana E. Forsythe, Bruce G. Buchanan, Geoffrey J. Gordon, 1989, Springer US.