LearningBayesianNetworksResources.md

Learning Bayesian Networks Resources

Books

• Learning Bayesian Networks, Richard Neapolitan, 2006

• Elements of Causal Inference: Foundations and Learning Algorithms, Jonas Peters, Dominik Janzing, Bernhard Scholkoepf, MIT Press, 2017

• Latent Variable Models: Introduction to Factor Analysis and Structural Equation Analysis, John C. Loehlin, 2004

articles

• Graphical Models, Exponential Families, and Variational Inference, M.J. Wainwright et al, 2008

• Bayesian Methods for Adaptive Models, D, MacKay, PhD Thesis, Caltech, 1992

• Causation, Prediction, and Search, Peter Spirtes, PhD Thesis, CMU, 2000

• An Overview of the Representation and Discovery of Causal Relationships using Bayesian Networks, Cooper, 1999

• Inference in Belief Networks: A Procedural Guide, C. Huang, Stanford, 1994

• Tutorial on Learning with Bayesian Networks, D. Heckerman, 2022

• Variational Inference: A Review for Statisticians, D. Blei et al, 2016

• The Causal Interpretation of Bayesian Networks, Korb and Nicholson, 2008

medium

• Bayesian Inference Problem, MCMC and Variational Inference with Joseph Rocca

• Causal Inference is a mindset, Ehud Karavani

• Using machine learning metrics to evaluate causal inference models, Ehud Karavani

• Solving Simpson's Paradox with Inverse Probability Weighting, Ehud Karavani

• Causal Inference with Continuous Treatments, Ehud Karavani

• Establishing Causality with Michal Oleszak (Part 1): The golden standard of randomized experiments

• Establishing Causality with Michal Oleszak (Part 2): Enforcing randomness via instrumental variables

• Establishing Causality with Michal Oleszak (Part 3): Regression discontinuity designs

• Causal Inference with Jane Huang (Part 1): Understanding the fundamentals

• Causal Inference with Jane Huang (Part 2): Selecting algorithms

• Causal Inference with Jane Huang (Part 3): Model validation and applications

• Causal Inference - Simpson's Paradox with Bruno Goncalves

• Causal Inference - Probability Theory with Bruno Goncalves

• Causal Inference - Graphs with Bruno Goncalves

• Causal Inference - Structural Causal Models with Bruno Goncalves

• Causal Inference - Chains and Forks with Bruno Goncalves

• Causal Inference - Colliders with Bruno Goncalves

• Causal Inference - d-separation with Bruno Goncalves

• Causal Inference - Model Testing and Causal Search with Bruno Goncalves

• Causal Inference - Interventions with Bruno Goncalves

• Causal Inference - The Adjustment Formula with Bruno Goncalves

• Causal Inference - Back Door Criterion with Bruno Goncalves

• Causal Inference - Front Door Criterion with Bruno Goncalves

• Causal Inference - Conditional Interventions and Covariate Specific Effects with Bruno Goncalves

• Causal Inference - Inverse Probability Weighing with Bruno Goncalves

• Causal Inference - Mediation with Bruno Goncalves

• Understanding V-Structures and the Critical Role They Play in Causal Validation and Causal Inference, Graham Harrison, March 2024

• 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

online videos, books, blogs and tutorials

• Keynote: The Mathematics of Causal Inference with Reflections on Machine Learning

MLSS Cadiz 2016, Jonas Peters

• Lecture 1

• Lecture 2

Lectures on Causality: Jonas Peters, Broad Institute, MIT, 2017

• Lecture 1

• Lecture 2

• Lecture 3

• Lecture 4

MIT 6.S897 Machine Learning for Healthcare, Spring 2019

• Causal Inference, Part 1

• Causal Inference, Part 2

UNC CCCR Speaker Series 2021

• Causal Inference: A Gentle Introduction with Michael Hudgens, UNC Chapel Hill, 2021

Brief Introduction to Causal Inference with Brady Neal, 2020

• Lecture 1 Brief Introduction to Causal Inference: Preview

• Lecture 2 Potential Outcomes

• Lecture 3 The Flow of Causation and Association in Graphs

• Lecture 4 Causal Models

• Lecture 5 Randomized Experiments and Identification

• Lecture 6 Estimation

• Lecture 7 Unobserved Confounding, Bounds, and Sensitivity Analysis

• Lecture 8 Instrumental Variables

• Lecture 9 Difference-in-Differences

• Alberto Abadie Guest Talk - Synthetic Controls

• Lecture 10 Causal Discovery from Observational Data

• Lecture 11 Causal Discovery from Interventions

• Lecture 12 Transfer Learning and Transportability

• Lecture 13 Counterfactuals and Mediation

Yoshua Bengio, Causal Representation Learning, 2020

• Yoshua Bengio Guest Talk - Towards Causal Representation Learning

Causal Representation Learning

• Causal Representation Learning Presentation, Chaochao Lu, Unviersity of Cambridge, 2021

Bayesian Methods

• Bayesian Methods for Hackers, Cam Davidson Pilon, online book on github