Peter Spirtes

发表

Methods to Expand Cell Signaling Models Using Automated Reading and Model Checking

Qinsi Wang, Natasa Miskov-Zivanov, Kai-Wen Liang, 2017, CMSB.

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 evaluation of machine-learning methods for predicting pneumonia mortality

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

A Hybrid Causal Search Algorithm for Latent Variable Models

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

Intervention, determinism, and the causal minimality condition

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

Using Artificial Intelligence to Aid in the Development of Causal Models

Richard Scheines, Clark Glymour, Peter Spirtes, 1990 .

Heuristic greedy search algorithms for latent variable models

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

Learning Measurement Models for Unobserved Variables

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

Strong Faithfulness and Uniform Consistency in Causal Inference pdf

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

Detection of Unfaithfulness and Robust Causal Inference

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

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

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

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 .

Reply to Freedman

Richard Scheines, Peter Spirtes, 1996 .

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

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

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 .

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

Peter Spirtes, P. Spirtes, 2013, UAI.

When causality matters for prediction

Peter Spirtes, Robert E. Tillman, 2010, NIPS Causality: Objectives and Assessment.

When causality matters for prediction: investigating the practical tradeoffs

Peter Spirtes, Robert E. Tillman, 2008 .

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

Richard Scheines, Clark Glymour, Peter Spirtes, 1992 .

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

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

Learning the Structure of Linear Latent Variable Models

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

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

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

Detecting Causal Relations in the Presence of Unmeasured Variables pdf

Peter Spirtes, P. Spirtes, 1991, UAI.

Tabu Search-Enhanced Graphical Models for Classification in High Dimensions

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

Exploring Causal Structure with the TETRAD Program

Richard Scheines, Clark Glymour, Peter Spirtes, 1988 .

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

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

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

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

Conditional Independence in Directed Cyclical Graphical Models Representing Feedback or Mixtures

Peter Spirtes, P. Spirtes, 1994 .

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

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

Finding latent variable models in large databases

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

A qualitative approach to causal modeling

Richard Scheines, Clark Glymour, Peter Spirtes, 1991 .

CHOICE OF UNITS AND THE CAUSAL MARKOV CONDITION

Jiji Zhang, Peter Spirtes, 2014 .

Automated discovery of linear feedback models

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

Causal discovery of linear acyclic models with arbitrary distributions

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

Towards Characterizing Markov Equivalence Classes for Directed Acyclic Graphs with Latent Variables pdf

Thomas S. Richardson, Jiji Zhang, Peter Spirtes, 2005, UAI.

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

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

A Theoretical Study of Y Structures for Causal Discovery pdf

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

Conditional Independence in Directed Cyclic Graphical Models for Feedback

Peter Spirtes, P. Spirtes, 1994 .

Discovering Cyclic Causal Models by Independent Components Analysis pdf

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

Equivalence of causal models with latent variables

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

Tetrad II: User's Manual

Richard Scheines, Christopher Meek, Clark Glymour, 1994 .

THE PROBLEMS OF SCIENCE WITHOUT EXPERIMENTS

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

Inferring causation from time series in Earth system sciences

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

Causal discovery and inference: concepts and recent methodological advances

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

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.

Causal Inference in the Presence of Latent Variables and Selection Bias

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

Graphical Models for the Identification of Causal Structures in Multivariate Time Series Models

Peter Spirtes, Alessio Moneta, 2006, JCIS.

The Computational and Experimental Complexity of Gene Perturbations for Regulatory Network Search

David Danks, Clark Glymour, Peter Spirtes, 2003 .

Estimating Causal Effects with Ancestral Graph Markov Models

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

Introduction to Causal Inference

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

An Anytime Algorithm for Causal Inference

Peter Spirtes, P. Spirtes, 2001, AISTATS.

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

Peter Grünwald, Peter Spirtes, Jeff Bilmes, 2010 .

Causation, Prediction, and Search, Second Edition

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

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

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

Semi-Instrumental Variables: A Test for Instrument Admissibility pdf

Richard Scheines, Peter Spirtes, Tianjiao Chu, 2001, UAI.

Automated Remote Sensing with Near Infrared Reflectance Spectra: Carbonate Recognition

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

Learning the Structure of a Nonstationary Vector Autoregression

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

A Fast Algorithm for Discovering Sparse Causal Graphs

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

12 – RUNNING TETRAD

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

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

Richard Scheines, Christopher Meek, Clark Glymour, 1999 .

Actual causation: a stone soup essay

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

TETRAD: Discovering Causal Structure.

Richard Scheines, Clark Glymour, Peter Spirtes, 1988, Multivariate behavioral research.

TETRAD II : tools for causal modeling

Richard Scheines, Christopher Meek, Clark Glymour, 1994 .

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

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

Regression, Causation and Prediction

Richard Scheines, Clark Glymour, Peter Spirtes, 1993 .

Vanishing TETRAD Differences and Model Structure

Alexander Kogan, Glenn Shafer, Peter Spirtes, 1996, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

Treatment selection by constraint propagation a case study in cutting fluid selection

Mark S. Fox, Peter Spirtes, James E. Mogush, 1988, Artif. Intell. Eng. Des. Anal. Manuf..

8 – CASE STUDIES

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

Discovery Algorithms for Causally Sufficient Structures

Richard Scheines, Clark Glymour, Peter Spirtes, 1993 .

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..

Scoring Ancestral Graph Models

Peter Spirtes, Thomas Richardson, 1999 .

Adjacency-Faithfulness and Conservative Causal Inference pdf

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

TETRAD II: Tools for Discovery

Richard Scheines, Christopher Meek, Clark Glymour, 1994 .

Design and Analysis of the Causation and Prediction Challenge

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

Building Latent Variable Models

Richard Scheines, Clark Glymour, Peter Spirtes, 1991 .

A Tutorial On Causal Inference

Peter Spirtes, 2009 .

Elaborating Linear Theories with Unmeasured Variables

Richard Scheines, Clark Glymour, Peter Spirtes, 1993 .

Data-driven covariate selection for nonparametric estimation of causal effects

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

7 – SIMULATION STUDIES

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

The three faces of faithfulness

Peter Spirtes, Jiji Zhang, 2015, Synthese.

The expected complexity of problem solving

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

Causal Clustering for 1-Factor Measurement Models

Richard Scheines, Erich Kummerfeld, Renjie Yang, 2016, KDD.

Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence (2010) pdf

Peter Grünwald, Peter Spirtes, 2012, ArXiv.

OBJECTIONS TO DISCOVERY BY COMPUTER

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

Nonlinear directed acyclic structure learning with weakly additive noise models

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

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

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

Automated Search for Causal Relations: Theory and Practice

Richard Scheines, Clark Glymour, Peter Spirtes, 2010 .

Mixed Graphical Models for Causal Analysis of Multi-modal Variables pdf

Clark Glymour, Joseph Ramsey, Andrew J. Sedgewick, 2017, ArXiv.

Learning Bayesian Networks with Discrete Variables from Data

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

Directed Cyclic Graphical Representations of Feedback Models

Peter Spirtes, P. Spirtes, 1995, UAI.

An experiment in causal discovery using a pneumona database

Gregory F. Cooper, Peter Spirtes, 1999, AISTATS.

A Uniformly Consistent Estimator of non-Gaussian Causal Effects Under the k-Triangle-Faithfulness Assumption pdf

Peter Spirtes, Shuyan Wang, P. Spirtes, 2021, CLeaR.

A Transformational Characterization of Markov Equivalence between DAGs with Latent Variables

Peter Spirtes, Jiji Zhang, 2005 .

Discovery Algorithms without Causal Sufficiency

Richard Scheines, Clark Glymour, Peter Spirtes, 1993 .

CAUSAL AND STATISTICAL MODELS

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

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

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

Calculating Tetrad constraints implied by directed acyclic graphs

Peter Spirtes, 1990 .

PCX : Markov Blanket Classification for Large Data Sets with Few Cases

Rema Padman, Clark Glymour, Joseph Ramsey, 2004 .

The Tetrad Project

Richard Scheines, Clark Glymour, Peter Spirtes, 1990 .

Causation and Prediction: Axioms and Explications

Richard Scheines, Clark Glymour, Peter Spirtes, 1993 .

Discussion of Causal Diagrams for Empirical Research by J. Pearl

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

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

Peter Spirtes, P. Spirtes, 1993 .

The Structure of the Unobserved

Richard Scheines, Clark Glymour, Peter Spirtes, 1993 .

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

Peter Spirtes, P. Spirtes, 1995 .

A Characterization of Markov Equivalence Classes for Ancestral Graphical Models

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

The dimensionality of mixed ancestral graphs

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

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

Peter Spirtes, P. Spirtes, 2005 .

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

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

Introduction and Advertisement

Richard Scheines, Clark Glymour, Peter Spirtes, 1993 .