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Understanding the causal relationships that underlie a system is a fundamental prerequisite to accurate decision-making.
Local computation with probabilities on graphical structures and their application to expert systems (with discussion)
Lauritzen, S. L. and Spiegelhalter, D. J · 1988
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The alarm monitoring system: A case study with two probabilistic inference techniques for belief networks
Beinlich, I. A., Suermondt, H. J., Chavez, R. M., and Cooper, G. F · 1989
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Learning in probabilistic expert systems
Spiegelhalter, D. J. and Cowell, R. G · 1992
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Causal inference and causal explanation with background knowledge
Meek, C · 1995
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Adaptive probabilistic networks with hidden variables
Binder, J., Koller, D., Russell, S., and Kanazawa, K · 1997
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The tetrad project: Constraint based aids to causal model specification
Scheines, R., Spirtes, P., Glymour, C., Meek, C., and Richardson, T · 1998
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Constructing bayesian network models of gene expression networks from microarray data
Spirtes, P., Glymour, C., and Scheines, R · 2000
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Optimal structure identification with greedy search
Chickering, D. M · 2002
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On the number of experiments sufficient and in the worst case necessary to identify all causal relations among n variables
Eberhardt, F., Glymour, C., and Scheines, R · 2005
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Causal protein-signaling networks derived from multiparameter single-cell data
Sachs, K., Perez, O., Pe’er, D., Lauffenburger, D. A., and Nolan, G. P · 2005
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Bayesian network learning algorithms using structural restrictions
de Campos, L. M. and Castellano, J. G · 2007
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Estimating high-dimensional intervention effects from observational data
Maathuis, M. H., Kalisch, M., and Bühlmann, P · 2009
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Learning bayesian networks with the bnlearn R package
Scutari, M · 2010
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Causal inference in the presence of latent variables and selection bias
Spirtes, P. L., Meek, C., and Richardson, T. S · 2013
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Learning bayesian networks with ancestral constraints
Chen, E. Y.-J., Shen, Y., Choi, A., and Darwiche, A · 2016
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Distinguishing cause from effect using observational data: Methods and benchmarks
Mooij, J. M., Peters, J., Janzing, D., Zscheischler, J., and Schölkopf, B · 2016
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Repair of partly misspecified causal diagrams
Oates, C., Kasza, J., Simpson, J., and Forbes, A · 2017
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Elements of causal inference: foundations and learning algorithms
Joint causal inference from multiple contexts
Mooij, J. M., Magliacane, S., and Claassen, T · 2020
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Constitutional ai: Harmlessness from ai feedback
Bai, Y., Kadavath, S., Kundu, S., Askell, A., Kernion, J., Jones, A., Chen, A., Goldie, A., Mirhoseini, A., McKinnon, C., et al · 2022
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Typing assumptions improve identification in causal discovery
Brouillard, P., Taslakian, P., Lacoste, A., Lachapelle, S., and Drouin, A · 2022
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Lmpriors: Pre-trained language models as task-specific priors
Choi, K., Cundy, C., Srivastava, S., and Ermon, S · 2022
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Investigating causal understanding in llms
Hobbhahn, M., Lieberum, T., and Seiler, D · 2022
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Peters, J., Janzing, D., and Schölkopf, B · 2017
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Bayesian network structure learning with side constraints
Li, A. and Beek, P · 2018
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Dags with no tears: Continuous optimization for structure learning
Zheng, X., Aragam, B., Ravikumar, P. K., and Xing, E. P · 2018
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Review of causal discovery methods based on graphical models
Glymour, C., Zhang, K., and Spirtes, P · 2019
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Inferring causation from time series in earth system sciences
Runge, J., Bathiany, S., Bollt, E., Camps-Valls, G., Coumou, D., Deyle, E., Glymour, C., Kretschmer, M., Mahecha, M. D., Muñoz-Marí, J., et al · 2019
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On the completeness of causal discovery in the presence of latent confounding with tiered background knowledge
Andrews, B., Spirtes, P., and Cooper, G. F · 2020
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Differentiable causal discovery from interventional data
Brouillard, P., Lachapelle, S., Lacoste, A., Lacoste-Julien, S., and Drouin, A · 2020
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Kadavath, S., Conerly, T., Askell, A., Henighan, T., Drain, D., Perez, E., Schiefer, N., Dodds, Z. H., DasSarma, N., Tran-Johnson, E., et al · 2022
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Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al · 2022
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Can foundation models talk causality?
Willig, M., Zečević, M., Dhami, D. S., and Kersting, K · 2022
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The impact of prior knowledge on causal structure learning
Constantinou, A. C., Guo, Z., and Kitson, N. K · 2023
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Causal reasoning and large language models: Opening a new frontier for causality
Kıcıman, E., Ness, R., Sharma, A., and Tan, C · 2023
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Can large language models build causal graphs?
Long, S., Schuster, T., and Piché, A · 2023
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Causal-discovery performance of chatgpt in the context of neuropathic pain diagnosis
Tu, R., Ma, C., and Zhang, C · 2023
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