Summary
In causality, graphical models make it way easier to get your head around complex causal relationships, turning them into diagrams that are much simpler to understand. This is super helpful when you’re trying to spot things that could skew your results, such as confounding variables, or when you’re figuring out how to measure cause and effect.
These models aren’t just about making things look neat. They give us a robust approach to understanding the assumptions behind our causal models and to discern when we can actually say one thing causes another. Judea Pearl’s work, especially his focus on DAGs in his book Causality: Models, Reasoning, and Inference, [1] has really set the stage here. By applying the basics of graph theory to causality, graphical models have totally transformed how we approach and analyze what causes what. Overall, the key takeaways from this chapter are the introduction of graphical models to understand and analyze causal...
