This week, I compared the difference between the Potential Outcome Model (Rubin Casual Model) and Structural Causal Model frameworks for causal inference. I failed to grasp the difference between the two. Still, I can share some resources on this matter. While I believe the comparison has more of a theoretical significance, I hope over time, I can fully understand it.

Resources

Chapter 3 Section 3.4 of Morgan, Stephen L., and Christopher Winship. Counterfactuals and Causal Inference: Methods and Principles for Social Research. Second Edition. Analytical Methods for Social Research. New York, NY: Cambridge University Press, 2015.

Chapter 3 Section 3.6.3 and Chapter 7 Section 7.4.4 of Pearl, Judea. Causality: Models, Reasoning, and Inference. Second edition, Reprinted with corrections. Cambridge New York, NY Port Melbourne New Delhi Singapore: Cambridge University Press, 2022.

Pearl, Judea. ‘Causal Inference in Statistics: An Overview’. Statistics Surveys 3, no. None (1 January 2009). https://doi.org/10.1214/09-SS057.

Myth 5 and 6 of Bollen, Kenneth A., and Judea Pearl. ‘Eight Myths About Causality and Structural Equation Models’. In Handbook of Causal Analysis for Social Research, edited by Stephen L. Morgan, 301–28. Handbooks of Sociology and Social Research. Dordrecht: Springer Netherlands, 2013. https://doi.org/10.1007/978-94-007-6094-3_15.

Ibeling, Duligur, and Thomas Icard. ‘Comparing Causal Frameworks: Potential Outcomes, Structural Models, Graphs, and Abstractions’. arXiv, 6 November 2023. https://doi.org/10.48550/arXiv.2306.14351.