RootCauseAnalysisResources.md

Causal Inference and Root Cause Analysis Resources

Books, Articles and Tutorials

• Philosophical Essays Concerning Human Understanding, David Hume, 1748

• The Direction of Time, Hans Reichenbach, edited by Maria Rechenbach, U California Press, 1956

• The Cement of The Universe, J.L. Mackie, 1980

• Probabilistic Causality, Ellery Eells, University of Wisconsin-Madison, 1991

• Causality - Models, Reasoning and Inference, Judea Pearl, 2nd Edition, 2009

• Counterfactuals and Causal Inference, Stephen Morgan and Christopher Winship, 2nd Edition, 2015

• Dynamic Bayesian Networks: Representation, Inference and Learning, K. Murphy, 2002, PhD Thesis

• The Book of Why: The New Science of Cause and Effect, J. Pearl, 2018

• Causation, Prediction and Search, P. Spirtes, C. Glymour, R. Scheines, CMU, 2001

• Causal Asymmetries, Daniel M. Hausman, 1998

• Generating Conditional Probabilities for Bayesian Networks: Easing the Knowledge Acquisition Problem, Balaram Das

• Causation, David Lewis, 1974

• Counterfactuals, David Lewis, 1973

• Redundant Causation, Michael McDermott, 1995

• Causation as Influence, David Lewis, 2000

• Probability and Causality, R. Otte, PhD Thesis, 1982

• Structural Equations with Latent variables: Computational Solutions for Modern Data Problems, E. van Kesteren, 1992, U.of Utrecht

• Elements of Causal Inference, J. Peters, D. Janzing, B. Schloekopf, 2017

• Introduction to Causal Inference from Machine Learning Perspective, Brady Neal, 2020

• Probabilistic Causality and Multiple Causation, Paul Humphreys, 1980

• Review of Granger-causality Fallacy, Marius Maziarz, 2014

• Scoring Ancestral Graph Models, Thomas Richardson and Richard Spirtes, 1999

• Causation, Prediction and Search, Peter Spirtes, Clark Glymour, Richard Scheines, PhD Thesis, 2000

• Dynamic Bayesian Networks - Representation, Inference, and Learning, Kevin P. Murphy, PhD Thesis, 2002

• Probabilistic Measures of Causal Strength, Branden Fitelson, Christopher Hitchock, 2011

  Companion notebook here

• Foundations of Probabilistic Theory of Causal Strength, J. Sprenger, 2017

• The Structural Theory of Causation, Judea Pearl, 2009

• Causal Laws and Effective Strategies, Nancy Cartwright, Stanford U., 1979

• Causal Explanation, David Lewis, 1986

• Introduction to Causal Inference, Peter Spirtes, Carnegie Mellon University, 2010

• An Introduction to Causal Inference, Richard Scheines, Carnegie Mellon University, 1995

• An Algorithmic Enquiry Concerning Causality, Samantha Kleinberg, PhD Thesis, 2010

• The Paper of How: Estimating Treatment Effects Using Front-Door Criterion, M. Bellemare, J. Bloem, 2020

• Identification of Causal Effects Using Instrumental Variables, Angrist, Imbens, Rubin, 1993

• Probabilistic Causality - A Rejoinder to Ellery Eells, Dupre, 1990

• Causally Powerful Processes, Dupre, 2021

• Foundations of Structural Causal Models with Cycles and Latent Variables, Stephan Bongers et al, 2021

• Discovering Cyclic Causal Structure, Thomas Richardson, Tech Report, 1996

• Identifying Independencies in Causal Graphs with Feedback, J. Pearl, R Dechter, 1996

• Single World Intervention Graphs (SWIFs): A Unification of the Counterfactual and Graphical Approaches to Causality, T. Richardson, J. Robins, 2013

• Applied Causal Inference Powered by ML and AI, V. Chernozhukov et al, 2024

Model Checking and Verification

• Automatic Verification Of Finite State Concurrent Systems Using Temporal Logic Specifications: A Practical Approach, Clarke, Emerson, Sistla, 1984

• The Complexity of Propositional Linear Temporal Logics, Sistla, Clarke, 1985

• A Logic for Reasoning about Time and Reliability, Hansson and Jonsson, 1994

• The Temporal Logic of Causal Structures, Kleinberg and Mishra, 2009

• An Algorithmic Enquiry Concerning Causality, Samantha Kleinberg, PhD thesis, 2010

• A New Introduction to Modal Logic, Hughes and Cresswell, 1996

Causal inference via Bayesian Networks

• Causality, Judea Pearl, 2nd edition, 2009

• Probabilistic Reasoning in Intelligent Systems, Judea Pearl, 1988

• Identifying Independence in Bayesian Networks, Geiger, Verma, Pearl, 1990

• Identifying Independencies in Causal Graphs with Feedback, J. Pearl and R. Dechter, 1996

• Dynamic Bayesian Networks: Representation, Inference, Learning, Kevin Murphy, PhD thesis, 2002

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

Causal Representation Learning

• Towards Causal Representation Learning, Bernhard Schölkopf et al, 2021

• Causal inference using invariant prediction: identification and confidence intervals, Jonas Peters et al, 2015

• Invariant Risk Minimization, Martin Arjovsky, L´eon Bottou et al, 2020

• Nonlinear Invariant Risk Minimization: A Causal Approach, Chaochao Lu et al, 2022

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

Causal Forecasting

• Causal Forecasting for Pricing, D. Schultz et al, Zalando SE, 2024

Structural Equations Modeling of Causality

• Causality, Judea Pearl, 2nd edition, 2009

• Structural Equations Models, Bielby and Hauser, 1977

• Conditional Independence in Directed Cyclic Graphical Models for Feedback, Peter Spirtes, 1994

• Identifying Independencies in Causal Graphs with Feedback, J. Pearl and R. Dechter, 1996

• The Structural Theory of Causation, Judea Pearl, 2009

• Structural Equations with Latent Variables: Computational Solutions for Modern Data Problems, Erik-Jan van Kesteren, 2021

• Foundations of Structural Causal Models with Cycles and Latent Variables, S. Bongers et al, 2021

• The Causal Foundations of Structural Equation Modeling, J Pearl, 2021

• Fifty Years of Structural Equation Modeling: A History of Generalization, Unification, and Difffusion, 2022

• Elements of Causal Inference, J. Peters, D. Janzing, B. Schloekopf, 2017

Judea Pearl's Causality Work

• Probabilistic Reasoning in Intelligent Systems, J. Pearl, 1988

• Causality: Models, Reasoning, and Inference, J. Pearl, 2006

• Causality, J. Pearl, 2009

• Causal Inference in Statistics - A Primer, J. Pearl, 2009

• Do-Calculus Revisited, Judea Pearl, Keynote Lecture, UAI-2012 Conference, Catalina, CA

• The Book of Why: The Science of Cause and Effect, J. Pearl, 2018

• more articles by Judea Pearl ...

Wesley C. Salmon's Causality Work

• Probabilistic Causality, Wesley C. Salmon, 1980

• Scientific Explanation and the Causal Structure of the World, Wesley C. Salmon, Princeton, 1984

• Causality And Explanation, Collection of 26 of Wesley C. Salmon's essays, 1987

David Lewis work on Counterfactuals

• Counterfactuals, David Lewis, 1973

• Causation, David Lewis, 1974

• Causation as Influence, David Lewis, 2000

Good's Causal Calculus Work

• A Causal Calculus, part I and II, A. J. Good, 1960

Branden Fitelson Work on Causal Strength

• Probabilistic Measures of Causal Strength, Chapter 29, Causality in The Sciences, 2011 (original print)

• Probabilistic Measures of Causal Strength, (standalone reprint)

• Probabilistic Measures of Causal Strength, companion notebook

Manipulability Theories and Causation

• Making Things Happen: A Theory of Causal Explanation, James Woodward, 2003

• Causation and Manipulability, James Woodward, 2003

Early attempts to define Causality with Graph-based stochastic models

• The Method of Path Coefficients, Sewall Wright, 1934

• Causal Inference from Cross-Lagged Correlation Coefficients: Fact or Fancy?, Peer Soelberg, MIT, 1967

• Identification and Estimation in Path Analysis with Unmeasured Variables, C.E. Werts et al, Princeton, 1971

• Recursive Causal Models, Harri Kiiveri, T.P. Speed, J.B. Carlin, 1981

• Gaussian Markov Distributions over Finite Graphs, T.P. Speed, H. Kiiveri, 1986

Richard Neapolitan: Bayesian Networks and related algorithms

• Learning Bayesian Networks, R. Neapolitan, 2004

CMU publications on Causality and ICA

https://www.cmu.edu/dietrich/causality/publications/

WCCI Causality Challenge 2008

First Causality Challenge organized by the Causality Workbench Team for the World Congress in Artificial Intelligence (WCCI), June 3, 2008, Hong Kong.

Collection of papers and discussion on variety of modeling techniques, focusing on predicting the effect of "interventions" performed by an external agent. All datasets which are used to train and test the models in this collection of papers are part of the challenge.

compendium book

Link for sharing problems and test methods: https://www.causality.inf.ethz.ch/cause-effect.php

Link for downloading the datasets in the challenge: https://www.causality.inf.ethz.ch/challenge.php

Medium

• Causality ML for Decision Making with Ryuta Yoshimatsu

• The Dual Roots of Belief Propagation and Causal Inference with Oliver Beige

  related papers:

  Identification of Causal Effects Using Instrumental Variables, J.D. Angrist, G.W. Imbens, D.B. Rubin, 1993

  Machine Learning Methods for Estimating Heterogeneous Causal Effects, Susan Athey, G.W. Imbens, 2015

• When Correlation is Better than Causation, Brittany Davis

• 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

• Attribution analysis: How to measure impact with Lisa Cohen (Part 1)

• Attribution analysis: How to measure impact with Lisa Cohen (Part 2)

• Regression and Causal Inference: Which Variables Should Be Added to the Model?, Vivekanda Das

• Causal Effects, Shawhin Talebi

• Causal Effects via Propensity Scores, Shawhin Talebi

• Causal Effects via the Do-operator, Shawhin Talebi

• Causal Inference, Shawhin Talebi

• Introduction to Causal Inference with Mert Atli

• Causal Discovery, Shawhin Talebi

• Understanding Causal Effects, Shawhin Talebi

• Causal Effects via Regression, Shawhin Talebi

• Causal Inference: a Four-Stage Framework, Leo Ware

• Double Machine Learning for Causal Inference from a Partially Linear Model, Zachary Clement

• Double Machine Learning for causal inference, Borja Velasco, June 2021

• Causal Inference with high-dimensional data: An evaluation of the Double Machine Learning Algorithm, Zachary Clement

• Explore and understand your data with a network of significant associations, Erdogan Taskesen

• The Power of Bayesian Causal Inference: A Comparative Analysis of Libraries to Reveal Hidden Causality in Your Dataset, Erdogan Taskesen

• A step-by-step guide in designing knowledge-driven models using Bayes theorem, Erdogan Taskesen

• A step-by-step guide in detecting causal relationships using Bayesian Structure Learning in Python, Erdogan Taskesen

• Introduction to Probabilistic Graphical Models, Branislav Hollander

• Propensity Score Trimming Using Python Package CausalInference with Amy@GrabNGoInfo

• Six Causal Inference Techniques Using Python with Thomas Caputo

• Causal Python - Level Up Your Causal Discovery Skills in Python (2023) with Aleksander Molak

• Double Machine Learning for causal inference with Borja Velasco, 2021

• Causal Python - Elon Musk's Tweet, Our Googling Habits, and Bayesian Synthetic Control with Aleksander Molak

• 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

• Introducing the Do-Sampler for Causal Inference with Adam Kelleher

  Jupyter notebook here Tutorial on Causal Inference and Counterfactual reasoning [here](https://causalinference.gitlab.io/kdd-tutorial/ Do-Sampler source code here Link to Judea Pearl's paper hereo

• Demystifying Causality: An Introduction to Causal Inference and Applications, Part 1, with IvanGor

• Demystifying Causality: An Introduction to Causal Inference, Part 2, with Ivan Gor

• Demistifying Causality: An Introduction to Causal Inference, Part 3, with Ivan Gor

  companion paper: here

• Demistifying Causality: An Introduction to Causal Inference, Part 4, with Ivan Gor

• The Need for Causal Inference with Robert Kuebler

• Causal Inference: Quasi-Experiments with Ian Ho

• Supply and Demand: Simultaneous Equations, not Simultaneous Causation with Ben Ogorek

  related paper: link

• Causal analysis with PyMC: Answering "What If?" with the new do operator with PyMC Labs

• BICauseTree: Bias-balancing Interpretable Causal Tree with Lucile Ter-Minassian

  related papers:

  Causal Inference Using Potential Outcomes - Design, Modeling, Decisions, Rubin, 2005, JASA

  Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead, Rudin, 2019

  RealCause: Realistic Causal Inference Benchmarking, Neal et al, 2021

• Automated Causal Detection with Todd Moses, 2024

  related papers:

  Illusions of causality: how they bias our everyday thinking and how they could be reduced, H. Matute et al, 2015

Videos

Microsoft Research 2013

• 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

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

Root Cause analysis with DoWhy

• PyConDE & PyData Berlin 2023 presentation on Root Cause Analysis with DoWhy

Causal Discovery

• The Causal Discovery Ladder: Unravelling Governing Equations and Beyond, Mihaela van der Schaar, April 2024, Online Causal Inference Seminar