Resources on Causal Inference

This collection aims to provide a comprehensive overview of causal inference, from foundational principles to advanced applications and practical tools.

Foundational Textbooks & Overviews

These resources offer broad introductions and systematic treatments of causal inference, suitable for those building a strong theoretical understanding.

Angrist, J. D., & Pischke, J.-S. (2009). Mostly harmless econometrics: An empiricist’s companion. Princeton University Press.
- Companion Website/Notes: A key text in econometrics that emphasizes practical approaches to causal inference using methods like instrumental variables and difference-in-differences. IDEAS/RePEc provides an abstract and citation information: https://ideas.repec.org/b/pup/pbooks/8769.html
Bechtel, W., & Richardson, R. C. (2010). Discovering complexity: Decomposition and localization in biological discovery. MIT Press.
- Companion Website/Notes: This book offers a good overview of approaches that seek to isolate mechanisms in complex systems, relevant for understanding mechanistic explanations of causality. William Bechtel’s website provides a description of this and related work: https://mechanism.ucsd.edu/~bill/
Cunningham, S. (2021). Causal Inference: The Mixtape. Yale University Press.
- Companion Website/Notes: Highly recommended for its accessible and contemporary introduction to causal inference methods in social sciences. This book has a fantastic free online HTML version with interactive R programming examples: https://www.scunning.com/mixtape.html
Morgan, S. L. (Ed.). (2013). Handbook of causal analysis for social research. Dordrecht: Springer Netherlands.
- Companion Website/Notes: This handbook is a more statistically heavy resource, providing a collection of chapters from leading scholars. ResearchGate offers an overview of the book’s contents: https://www.researchgate.net/publication/321274794_Handbook_of_Causal_Analysis_for_Social_Research
Morgan, S. L., & Winship, C. (2014). Counterfactuals and causal inference: Methods and principles for social research. Cambridge University Press.
- Companion Website/Notes: This book is a widely cited text in social sciences for causal inference. The Cambridge University Press page provides a good overview: https://www.cambridge.org/core/books/counterfactuals-and-causal-inference/5CC81E6DF63C5E5A8B88F79D45E1D1B7
Pearl, J. (2018). The Book of Why: The New Science of Cause and Effect. Basic Books.
- Companion Website/Notes: A seminal work by one of the pioneers of causal inference, introducing the concept of the “ladder of causation” and the power of causal diagrams (DAGs). The Wikipedia page provides a good summary of the book’s contents: https://en.wikipedia.org/wiki/The_Book_of_Why
Smaldino, P. E. (2023). Modeling Social Behavior: Mathematical and Agent-Based Models of Social Dynamics and Cultural Evolution. Princeton University Press.
- Companion Website/Notes: While broader than just causal inference, this book provides a unified introduction to modeling social behavior, including concepts relevant to understanding complex causal systems. Dokumen.pub offers a description: https://dokumen.pub/modeling-social-behavior-mathematical-and-agent-based-models-of-social-dynamics-and-cultural-evolution-9780691224152-9780691224138.html

Specific Journal Articles & Methodological Papers

These articles delve into specific aspects, challenges, and applications of causal inference.

Akbari, K., Winter, S., & Tomko, M. (2023). Spatial Causality: A Systematic Review on Spatial Causal Inference. Geographical Analysis, 55(1), 56-89. https://doi.org/10.1111/gean.12312
- Notes: This systematic review focuses on challenges and methods for causal inference in spatial processes, highlighting issues like spatial dependency and heterogeneity.
DiFranza JR, Wellman RJ, Sargent JD, Weitzman M, Hipple BJ, Winickoff JP, Tobacco Consortium, Center for Child Health Research of the American Academy of Pediatrics. (2006). Tobacco promotion and the initiation of tobacco use: assessing the evidence for causality. Pediatrics, 117(6), e1237-48.
- Notes: An “old school” but straightforward resource on assessing evidence for causality, particularly in the context of public health.
Imai, K., King, G., & Stuart, E. A. (2008). Misunderstandings between experimentalists and observationalists about causal inference. Journal of the Royal Statistical Society: Series A (Statistics in Society), 171(2), 481-502. https://doi.org/10.1111/j.1467-985X.2007.00527.x
- Notes: This article addresses common misunderstandings and bridges the gap between experimental and observational approaches to causal inference.
Lundberg, I., Johnson, R., & Stewart, B. M. (2021). What Is Your Estimand? Defining the Target Quantity Connects Statistical Evidence to Theory. American Sociological Review, 86(3), 532-565. https://doi.org/10.1177/00031224211004187
- Notes: This paper emphasizes the importance of clearly defining the estimand (the target quantity) in causal inference, linking statistical evidence to theory.
Mi, R. Z., Toma, C. L., & Mo, S. (In press). Media-based task-switching as self-control failure: Two experimental studies. Journal of Media Psychology. https://doi.org/10.1027/1864-1105/a000473
- Notes: This publication presents two experimental studies related to media-based task-switching as self-control failure. While the DOI might not be fully active yet, ScienceOpen provides an abstract and author information: https://www.scienceopen.com/document?vid=6c9aad75-5a01-4f00-b88f-874d2e365f76
Miller, L. C., Shaikh, S. J., Jeong, D. C., Wang, L., Gillig, T. K., Godoy, C. G., … Read, S. J. (2019). Causal Inference in Generalizable Environments: Systematic Representative Design. Psychological Inquiry, 30(4), 173–202. https://doi.org/10.1080/1047840X.2019.1693866
- Notes: This paper discusses causal inference in the context of generalizable environments and systematic representative design.
Rohrer, J. M. (2018). Thinking Clearly About Correlations and Causation: Graphical Models for Observational Data. Advances in Methods and Practices in Psychological Science, 1(1), 27-42. https://doi.org/10.1177/2515245917745629
- Notes: An excellent introductory resource to Directed Acyclic Graphs (DAGs) for causal inference. GESIS Training offers short courses on this topic: https://training.gesis.org/?site=pDetails&child=full&pID=0x95D2949CC24E4C3E87DE9AB08493E1E1&subID=0x50599DF8F92A45E9B52ED4C99E683FDB&lang=en_US&lang=de_DE

Software Packages & Tools

These resources provide practical implementations for conducting causal inference analyses.

Applied Causal Inference Powered by ML and AI
- Description: This resource explores the fusion of machine learning and causal inference, though it’s noted as potentially challenging for many in the field. It often includes Jupyter Notebooks for practical application.
- Website: https://github.com/CausalAIBook (Refers to a book and notebooks)
Causal Inference in R
- Description: A blog and resource hub for R packages and information related to causal inference, designed for ease of use and compatibility with the Tidyverse.
- Website: https://r-causal.github.io/r-causal-blog/
CausalImpact R package
- Description: Implements a Bayesian approach to estimating the causal effect of a designed intervention on a time series.
- Website: https://google.github.io/CausalImpact/ (CRAN page: https://cran.r-project.org/package=CausalImpact)
Siena (Software for Statistical Network Analysis)
- Description: A package for longitudinal network modeling, useful for establishing causality in network studies within communication and other fields.
- Website: https://www.stats.ox.ac.uk/~snijders/siena/ (Note: A Google search for “Siena software package website” also brings up “Sienna” by NREL, which is a different software for energy analysis. Ensure you are looking for the social network analysis software.)
TERGM models (Temporal Exponential Random Graph Models)
- Description: An integrated set of extensions to the ergm package to analyze and simulate network evolution based on exponential-family random graph models. Often used in conjunction with Siena for longitudinal network analysis.
- Website: Part of the statnet suite of packages: https://statnet.org/workshop-tergm/ (CRAN page: https://cran.r-project.org/package=tergm)

Podcasts & Working Papers

Freakonomics Radio: Policymaking Is Not a Science — Yet (Update)
- Description: This podcast episode discusses the challenges of translating scientific findings into effective public policy and the role of “implementation scientists.”
- Episode Link: https://freakonomics.com/podcast/policymaking-is-not-a-science-yet-update/#:~:text=I%20think%20we’re%20just,a%20little%20bit%20more%20perfect.&text=Freakonomics%20Radio%20is%20produced%20by,our%20composer%20is%20Luis%20Guerra
Working Paper on Limitations of RCTs in Sociological Literature
- Description: A forthcoming working paper exploring the limitations of Randomized Controlled Trials (RCTs) within the sociological literature. A previous, more limited version appeared here: https://www.socio-journal.ch/article/view/5792
- Notes: The contributor has offered to share the paper when it’s ready for publication in May.

ICA Slides

https://docs.google.com/presentation/d/1PGnCh72dK26xM8MANBJ0F7gNey7lyENpdKFdTCCO4bg/edit?usp=sharing

Potential Future Initiatives

Literature on competing approaches to and about causality: A suggestion to broaden the website to include diverse philosophical and methodological perspectives on causality beyond specific statistical techniques.
Practical, user-friendly guide for translating causal research into social impact initiatives: This idea suggests creating resources that bridge the gap between causal findings and actionable strategies, possibly through partnerships with organizations or nonprofits.
“R&R stage” papers: Several contributors mentioned papers currently in “revise and resubmit” stage; these could be added once accepted and published.

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