21.3 The 7 Tools of Structural Causal Models

Pearl’s Structural Causal Model (SCM) framework provides tools for causal inference (Pearl 2019):

1. Encoding Causal Assumptions – Using causal graphs for transparency and testability.
2. Do-Calculus – Controlling for confounding using the backdoor criterion.
3. Algorithmization of Counterfactuals – Modeling “what if?” scenarios.
4. Mediation Analysis – Understanding direct vs. indirect effects.
5. External Validity & Adaptability – Addressing selection bias and domain adaptation.
6. Handling Missing Data – Using causal methods to infer missing information.
7. Causal Discovery – Learning causal relationships from data using:
   • d-separation
   • Functional decomposition (Hoyer et al. 2008)
   • Spontaneous local changes (Pearl 2014)

References

Hoyer, Patrik, Dominik Janzing, Joris M Mooij, Jonas Peters, and Bernhard Schölkopf. 2008. “Nonlinear Causal Discovery with Additive Noise Models.” Advances in Neural Information Processing Systems 21.

Pearl, Judea. 2014. “Graphical Models for Probabilistic and Causal Reasoning.” Computing Handbook, 3rd Ed.(1), 44–41.

———. 2019. “The Seven Tools of Causal Inference, with Reflections on Machine Learning.” Communications of the ACM 62 (3): 54–60.