A Structural Causal Model has three parts : 
1. Graphical Model
2. Structural Equations
3. Counterfactuals and interventional logic

University of Michigan - Ann Arbor

Pearl, Judea. “The Seven Tools of Causal Inference, with Reflections on Machine Learning.” Communications of the ACM, vol. 62, no. 3, 2019, pp. 54–60., doi:10.1145/3241036.

https://dl.acm.org/doi/pdf/10.1145/3241036

The Seven Tools of Causal Inference, with Reﬂections on Machine Learning

Obstacles that machine learning faces : 
1. Adaptability
2. Explainability
3. Understanding Causal Relationships

Three obstacles for Machine Learning

The seven tasks the author thinks SCMs accomplish are : 
1. Encoding Causal Assumptions : Allow Transparency and Testability
2. Allows control for confounding and Do-calculus
3. Helps generate algorithms for counterfactuals
4. Mediation analysis and assessment of effects
5. Adaptability, sample selection, validity
6. Allows for missing data
7. Allows causal discovery

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