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Keyword

conditional independence testing

Description: Statistical methods for testing whether two variables are independent given one or more other variables.
OpenAlex ID: keywords/conditional-independence-tests
Learning Bayesian Networks with the bnlearn R Package
2010 · Marco Scutari · Journal of Statistical Software · 1,738
Detecting and quantifying causal associations in large nonlinear time series datasets
2019 · Jakob Runge, Peer Nowack, et al. · Science Advances · 964
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Learning Bayesian networks from data: An information-theory based approach
2002 · Jie Cheng, Russell Greiner, et al. · Artificial Intelligence · 804
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Estimating high-dimensional directed acyclic graphs with the PC-algorithm
2005 · Kalisch, Markus, Buehlmann, Peter · arXiv (Cornell University) · 704
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Detecting and quantifying causal associations in large nonlinear time series datasets
2019 · Runge, J., Nowack, P., et al. · Publication Database PIK (Potsdam Institute for Climate Impact Research (PIK)) · 692
Kernel-based Conditional Independence Test and Application in Causal Discovery
2012 · Zhang, Kun, Peters, Jonas, et al. · arXiv (Cornell University) · 373
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Estimating High-Dimensional Directed Acyclic Graphs with the PC-Algorithm
2007 · KalischMarkus, BühlmannPeter · Journal of Machine Learning Research · 330
Sufficient Dimension Reduction via Inverse Regression
2005 · R. Dennis Cook, Liqiang Ni · Journal of the American Statistical Association · 318
A Scoring Function for Learning Bayesian Networks based on Mutual Information and Conditional Independence Tests
2006 · Luis M. de Campos · 308
That’s a Lot to Process! Pitfalls of Popular Path Models
2022 · Julia M. Rohrer, Paul Hünermund, et al. · Advances in Methods and Practices in Psychological Science · 260
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Who learns better Bayesian network structures: Accuracy and speed of structure learning algorithms
2019 · Marco Scutari, Catharina Elisabeth Graafland, et al. · International Journal of Approximate Reasoning · 252
Learning belief networks from data
1997 · Jie Cheng, David A. Bell, et al. · ACM International Conference on Information and Knowledge Management (CIKM) · 239
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Weights-of-evidence model applied to landslide susceptibility mapping in a tropical hilly area
2010 · Biswajeet Pradhan, Hyun-Joo Oh, et al. · Geomatics Natural Hazards and Risk · 236
Learning Bayesian Networks with the bnlearn R Package
2009 · Scutari, Marco · arXiv (Cornell University) · 231
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Searching for the Causal Structure of a Vector Autoregression*
2003 · Selva Demiralp, Kevin D. Hoover · Oxford Bulletin of Economics and Statistics · 228
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Landslide susceptibility analysis and verification using the Bayesian probability model
2002 · Saro Lee, Jaewon Choi, et al. · Environmental Geology · 214
The Hardness of Conditional Independence Testing and the Generalised Covariance Measure
2019 · Shah, Rajen D, Peters, Jonas · Apollo (University of Cambridge) · 176
Construction of Bayesian network structures from data: A brief survey and an efficient algorithm
1995 · Moninder Singh, Marco Valtorta · International Journal of Approximate Reasoning · 175
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Approximate Kernel-Based Conditional Independence Tests for Fast Non-Parametric Causal Discovery
2019 · Strobl Eric V., Zhang Kun, et al. · DOAJ (DOAJ: Directory of Open Access Journals) · 159
Learning Bayesian Networks from Data: An Efficient Approach Based on Information Theory
1999 · Jie Cheng, David Bell, et al. · 153
Bayesian Network Constraint-Based Structure Learning Algorithms: Parallel and Optimized Implementations in the bnlearn R Package
2017 · Marco Scutari · Journal of Statistical Software · 150
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Response Models for Mixed Binary and Quantitative Variables
1992 · D. R. COX, NANNY WERMUTH · Biometrika · 150
The hardness of conditional independence testing and the generalised covariance measure
2020 · Rajen D. Shah, Jonas Peters · The Annals of Statistics · 146
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Conditional Distance Correlation
2015 · Xueqin Wang, Wenliang Pan, et al. · Journal of the American Statistical Association · 132
Accurate Markov Boundary Discovery for Causal Feature Selection
2019 · Xingyu Wu, Bingbing Jiang, et al. · IEEE Transactions on Cybernetics · 120
Works count: 1,212
Citation count: 17,950
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