Resources

Comprehensive Causal Inference Resource Library

This collection brings together the best resources for learning, teaching, and applying causal inference methods. All resources are curated for quality, accessibility, and practical utility.

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Textbooks & eBooks

Foundational textbooks, free online books, and essential readings for every level of expertise.

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Code Repositories

R and Python code examples, replication materials, and implementation guides for causal methods.

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Course Materials

Slides, lecture notes, problem sets, and syllabi from university courses on causal inference.

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Research Papers

Key papers, literature reviews, and methodological advances in causal inference.

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Software Tools

R packages, Python libraries, and specialized software for causal analysis.

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Video Lectures

Recorded lectures, tutorials, and conference talks from experts in the field.

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Textbooks & eBooks

Foundational Texts

  1. Hernán, M. A., & Robins, J. M. (2020). Causal Inference: What If. Chapman & Hall/CRC.
    • Free online version
    • Comprehensive, accessible introduction to potential outcomes framework
    • Focus on epidemiology but widely applicable
  2. Imbens, G. W., & Rubin, D. B. (2015). Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction. Cambridge University Press.
    • Rigorous treatment of Rubin causal model
    • Excellent for graduate students and researchers
  3. Angrist, J. D., & Pischke, J. S. (2014). Mastering 'Metrics: The Path from Cause to Effect. Princeton University Press.
    • Intuitive introduction to five key methods: RCTs, regression, IV, RDD, DiD
    • Engaging writing style with real-world examples
  4. Pearl, J., Glymour, M., & Jewell, N. P. (2016). Causal Inference in Statistics: A Primer. Wiley.
    • Introduction to causal diagrams and do-calculus
    • Complementary to potential outcomes approach

Advanced Texts

  1. Pearl, J. (2009). Causality: Models, Reasoning, and Inference (2nd ed.). Cambridge University Press.
    • Seminal work on structural causal models
    • Advanced mathematical treatment
  2. Morgan, S. L., & Winship, C. (2014). Counterfactuals and Causal Inference: Methods and Principles for Social Research (2nd ed.). Cambridge University Press.
    • Bridges potential outcomes and structural equation modeling
    • Strong focus on social science applications
  3. Cunningham, S. (2021). Causal Inference: The Mixtape. Yale University Press.
    • Free online version
    • Practical focus with Stata, R, and Python code
    • Covers modern methods including synthetic control

Specialized Topics

  1. Athey, S., & Imbens, G. W. (2019). The Econometrics of Randomized Experiments. In Handbook of Field Experiments.
    • Advanced treatment of experimental design and analysis
  2. Van der Laan, M. J., & Rose, S. (2011). Targeted Learning: Causal Inference for Observational and Experimental Data. Springer.
    • Advanced statistical framework for causal inference
    • Focus on machine learning integration

Free Online Resources

  1. The Effect: An Introduction to Research Design and Causality
    • Online book by Nick Huntington-Klein
    • Interactive examples and visualizations
  2. Causal Data Science

Code Repositories & Examples

R Resources

  1. Causal Inference Cheat Sheet
  2. Causal Impact R Package
    • CRAN
    • Bayesian structural time-series for causal inference
  3. MatchIt Package
    • Documentation
    • Nonparametric preprocessing for parametric causal inference
  4. fixest Package
    • Website
    • Fast fixed-effects estimation for difference-in-differences

Python Resources

  1. DoWhy Library
    • GitHub
    • End-to-end causal inference library from Microsoft Research
  2. Causal ML Package
  3. EconML Library
    • GitHub
    • Heterogeneous treatment effects estimation

Interactive Tutorials

  1. Causal Inference with R
  2. Python Causal Inference Tutorials

Replication Materials

  1. Social Science Reproduction Project
    • Website
    • Code and data for replicating published studies
  2. AEA RCT Registry
    • Website
    • Pre-analysis plans and replication materials

Course Materials

University Course Syllabi

  1. Harvard University - STAT 186: Causal Inference
    • Syllabus
    • Taught by Prof. Edoardo Airoldi
  2. Stanford University - MS&E 226: Fundamentals of Causal Inference
  3. UC Berkeley - STAT 240: Causal Inference
    • Materials
    • Taught by Prof. Rina Foygel Barber

Lecture Slides & Notes

  1. Causal Inference Slides Collection
    • GitHub
    • Comprehensive slide decks covering multiple methods
  2. Potential Outcomes Framework
    • PDF Notes
    • Clear introduction to Rubin causal model

Problem Sets & Exams

  1. MIT 14.387: Applied Econometrics
  2. Princeton ORF 524: Causal Inference

Key Research Papers

Foundational Papers

  1. Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66(5), 688-701.
    • Introduces potential outcomes framework
  2. Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41-55.
    • Foundation for propensity score methods
  3. Angrist, J. D., Imbens, G. W., & Rubin, D. B. (1996). Identification of causal effects using instrumental variables. Journal of the American Statistical Association, 91(434), 444-455.
    • Formalizes IV estimation

Modern Advances

  1. Athey, S., & Imbens, G. (2016). Recursive partitioning for heterogeneous causal effects. Proceedings of the National Academy of Sciences, 113(27), 7353-7360.
    • Causal forests for heterogeneous treatment effects
  2. Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21(1), C1-C68.
    • Machine learning for causal inference
  3. Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic control methods for comparative case studies: Estimating the effect of California's tobacco control program. Journal of the American Statistical Association, 105(490), 493-505.
    • Synthetic control method

Literature Reviews

  1. Imbens, G. W. (2020). Potential outcome and directed acyclic graph approaches to causality: Relevance for empirical practice in economics. Journal of Economic Literature, 58(4), 1129-1179.
    • Comprehensive review of causal inference in economics
  2. Hernán, M. A. (2018). The C-word: Scientific euphemisms do not improve causal inference from observational data. American Journal of Public Health, 108(5), 616-619.
    • Clear thinking about causal language

Software Tools

R Packages

  1. causalverse - Meta-package for causal inference
  2. MatchIt - Matching methods for causal inference
  3. WeightIt - Weighting methods for causal inference
  4. fixest - Fast fixed-effects estimation
  5. ivpack - Instrumental variables estimation
  6. rdrobust - Regression discontinuity
  7. gsynth - Generalized synthetic control
  8. grf - Generalized random forests for causal inference
  9. DoubleML - Double machine learning
  10. medflex - Mediation analysis

Python Libraries

  1. DoWhy - End-to-end causal inference
  2. CausalML - Machine learning for causal inference
  3. EconML - Heterogeneous treatment effects
  4. causalnex - Causal discovery and reasoning
  5. pgmpy - Probabilistic graphical models
  6. lingam - Linear non-Gaussian causal models

Specialized Software

  1. DAGitty - Web tool for drawing and analyzing DAGs
  2. Causal Fusion - Platform for causal discovery
  3. Tetrad - Software for causal modeling

Video Lectures & Tutorials

Lecture Series

  1. MIT OpenCourseWare: Causal Inference
  2. Harvard Data Science: Causal Inference
  3. Stanford Causal Inference Seminar

Conference Talks

  1. Causal Data Science Meeting
  2. Atlantic Causal Inference Conference

Tutorial Videos

  1. Causal Inference with R - DataCamp
    • Course
    • Interactive video tutorials
  2. Python Causal Inference - YouTube Tutorials

Contributing to This Resource Library

This resource library is community-maintained. To suggest additions or corrections:

  1. Submit a resource: Email zephyr.v@outlook.com with:
    • Resource title and description
    • URL or citation
    • Brief justification for inclusion
    • Your name (optional, for acknowledgment)
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"The most exciting phrase to hear in science, the one that heralds new discoveries, is not 'Eureka!' but 'That's funny...'"
— Isaac Asimov