The seven tools of causal inference, with reflections on machine learning

Pearl, Judea·2019·Communications of the ACM

Cited by (659)

Items 401–500 of 659

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  33. A graph-based Seq2Seq framework for causal discovery and out-of-distribution forecasting in building energy systems
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  34. From correlation to causality: Identifying potential environmental drivers of pathogenic antibiotic-resistant bacteria in river water using causal machine learning
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  36. Analysis of Visualized Complex Reaction Network in Low- Temperature Molecular Plasma
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  66. Machine Reasoning to Assess Pandemics Risks: Case of USS Theodore Roosevelt
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  67. Path-specific Effects Based on Information Accounts of Causality
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  76. A Scoping Review of Earth Observation and Machine Learning for Causal Inference: Implications for the Geography of Poverty
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  78. Editorial notes: Mechanisms as a unifying construct for CSCL research
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  79. Theopolis Monk: Envisioning a Future of A.I. Public Service
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  80. The many faces of deep learning
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  81. Tasks and methods of Big Data analysis (a survey)
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  83. A Note on Artificial Intelligence and Statistics
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  84. Causal Discovery of Linear Non-Gaussian Acyclic Model with Small Samples
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  85. Adversarial Examples and the Deeper Riddle of Induction: The Need for a Theory of Artifacts in Deep Learning
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  86. Learning Temporal Causal Sequence Relationships
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  87. Tools Of Causal Inference: Review and Prospects
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  90. Reply on CC1
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  93. Comment on gmd-2021-196
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  99. Certainty and Singular Causal Judgment
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