Resources

Learning Pathways

🎯 Start Here: Methods in Causal Inference Series

A four-part tutorial series published in Evolutionary Human Sciences that provides a systematic introduction to causal inference methods:

Part 1: Causal Diagrams and Confounding
🟢 Beginner • Essential concepts, DAGs, and confounding bias

Part 2: Interaction, Mediation, and Time-Varying Treatments
🟡 Intermediate • Effect modification and complex causal structures

Part 3: Measurement Error and External Validity
🟡 Intermediate • Critical for cross-cultural research

Part 4: Confounding in Experiments
🔴 Advanced • Experimental design considerations

TipGetting Started with R & Causal Tools

Software & Tools

R Packages

margot (migrating to margotverse)
A framework for causal inference with panel data, supporting doubly robust estimation and sensitivity analyses. - Documentation - Source Code - Installation: devtools::install_github("go-bayes/margot")

WarningPackage Migration Notice

The margot package is currently being restructured into the margotverse ecosystem. This migration will provide a more modular and maintainable framework for causal inference.

ggdag
Create directed acyclic graphs (DAGs) for causal inference. - Documentation - CRAN - Installation: install.packages("ggdag")

WeightIt
Weighting for covariate balance in observational studies. - Documentation - CRAN - Installation: install.packages("WeightIt")

boilerplate
Tools for generating standardised boilerplate text and documentation for reproducible research. - Documentation - CRAN - Installation: install.packages("boilerplate")

Essential Readings

Core Readings - Barrett, M. (2023). ggdag: Analyze and Create Elegant Directed Acyclic Graphs - Suzuki, E. et al. (2020). Causal Diagrams: Pitfalls and Tips 🟡 - Neal, B. (2020). Introduction to Causal Inference - Chapter 3 🟢

On the Importance of Timing in Data - Hernán, M.A. & Robins, J.M. (2024). Causal Inference: What If - Chapter 6 🔴

Tutorial - VanderWeele, T.J. and Knol, M.J. (2014). A tutorial on interaction

Foundational Papers - VanderWeele, T.J. (2007). Four Types of Effect Modification 🟡 - VanderWeele, T.J. (2009). On the Distinction Between Interaction and Effect Modification 🟡

Applied Methods - Hernán, M.A. & Robins, J.M. (2024). Causal Inference: What If - Chapters 4-5 🟡

Measurement Theory - VanderWeele, T.J. (2022). Constructed Measures and Causal Inference 🔴 - Fischer, R. & Karl, J.A. (2019). A Primer to (Cross-Cultural) Multi-Group Invariance Testing 🟡

Cross-Cultural Methods - He, J. & Van de Vijver, F.J.R. (2012). Bias and Equivalence in Cross-Cultural Research 🟡 - Harkness, J.A. (2003). Questionnaire Translation 🟢

Selection Bias - Hernán, M.A. et al. (2004). A Structural Approach to Selection Bias 🔴 - Hernán, M.A. (2017). Selection Without Colliders 🔴

Measurement Error - Hernán, M.A. & Cole, S.R. (2009). Causal Diagrams for Measurement Error 🟡

Implementation Guides - Bulbulia, J.A. (2024). A Practical Guide to Causal Inference 🟢 - Hoffman, K.M. et al. (2023). Comparison Groups in Propensity Score Analysis 🟡

Outcome-Wide Approaches - VanderWeele, T.J. et al. (2020). Outcome-Wide Longitudinal Designs 🟡

Key Papers - Athey, S. & Wager, S. (2019). Estimating Treatment Effects with Causal Forests: An Application 🟡 - Wager, S. & Athey, S. (2018). Estimation and Inference of Heterogeneous Treatment Effects using Random Forests 🔴 - Künzel, S.R. et al. (2019). Metalearners for Estimating Heterogeneous Treatment Effects using Machine Learning 🟡

Practical Applications - Davis, J. & Heller, S.B. (2017). Using Causal Forests to Predict Treatment Heterogeneity 🟢 - Athey, S. & Imbens, G.W. (2016). Recursive Partitioning for Heterogeneous Causal Effects 🟡

Video Library

How Do We Learn What Works?

Miguel Hernán

Measurement Constructs

Tyler VanderWeele

Effect Modification/ Heterogeneity

Stijn Vansteelandt & Betsy Ogburn

Introduction to Causal Forests

Susan Athey & Stefan Wager

Technical Deep Dive: Causal Forests

Stefan Wager

How Traditional Statistical Mediation Analysis Fails

Stijn Vansteelandt

Design

Synthetic Control Methods

Alberto Abadie

Difference-in-Differences

Paul Goldsmith-Pinkham

Instrumental Variables

Brady Neal

Introduction to Causal Inference

Richard McElreath

Educational Materials

Workshop Materials

Reference Texts

Core Textbooks

Causal Inference: What If
Miguel A. Hernán & James M. Robins
Free Book & Resources • The definitive text on causal inference methods with code examples

Explanation in Causal Inference
Tyler VanderWeele
Link to OUP page * I think this best book ever written on causal mediation analysis and interaction – and arguably one of the best books every written in statistics! Expensive, unfortunately, but worth checking out of the library (or purchasing if you have the means).

The Effect
Nick Huntington-Klein
Free Online • Practical guide with good visualisations. Accessible.

Additional Resources

  • Statistical Rethinking by Richard McElreath - Bayesian approach to causal inference
  • Counterfactuals and Causal Inference by Morgan & Winship - Social science applications
  • Targeted Learning by van der Laan & Rose - Machine learning for causal inference

Quick Reference

Note🎯 Where to Start?
  1. New to causal inference? → Start with our four-part tutorial series
  2. Need DAGs? → Check out the ggdag tutorials
  3. Ready to analyze? → Install margot and follow our practical guide
  4. Cross-cultural research? → See Part 3 of our series
TipContributing

Have a resource to suggest? Found a broken link? Please contact us or submit an issue on our GitHub repository.