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Bayesian Networks and Decision Graphs, 2 edition, Finn Jensen, Springer, 2007
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Introduction to Bayesian Networks, Timo Koski, John Noble, 2009
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Graphical Models, Exponential Families, and Variational Inference, M.J. Wainwright et al, 2008
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Bayesian Methods for Adaptive Models, D, MacKay, PhD Thesis, Caltech, 1992
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Causation, Prediction, and Search, Peter Spirtes, PhD Thesis, CMU, 2000
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Inference in Belief Networks: A Procedural Guide, C. Huang, Stanford, 1994
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Tutorial on Learning with Bayesian Networks, D. Heckerman, 2022
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Variational Inference: A Review for Statisticians, D. Blei et al, 2016
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The Causal Interpretation of Bayesian Networks, Korb and Nicholson, 2008
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Bayesian Inference Problem, MCMC and Variational Inference with Joseph Rocca
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Using machine learning metrics to evaluate causal inference models, Ehud Karavani
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Solving Simpson's Paradox with Inverse Probability Weighting, Ehud Karavani
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Establishing Causality with Michal Oleszak (Part 1): The golden standard of randomized experiments
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Establishing Causality with Michal Oleszak (Part 2): Enforcing randomness via instrumental variables
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Establishing Causality with Michal Oleszak (Part 3): Regression discontinuity designs
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Causal Inference with Jane Huang (Part 1): Understanding the fundamentals
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Causal Inference with Jane Huang (Part 2): Selecting algorithms
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Causal Inference with Jane Huang (Part 3): Model validation and applications
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Causal Inference - Structural Causal Models with Bruno Goncalves
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Causal Inference - Model Testing and Causal Search with Bruno Goncalves
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Causal Inference - The Adjustment Formula with Bruno Goncalves
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Causal Inference - Front Door Criterion with Bruno Goncalves
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Causal Inference - Conditional Interventions and Covariate Specific Effects with Bruno Goncalves
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Causal Inference - Inverse Probability Weighing with Bruno Goncalves
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The Dual Roots of Belief Propagation and Causal Inference with Oliver Beige
related paper: Machine Learning Methods for Estimating Heterogenous Causal Effects, Susan Athey, Guido Imbens, 2015