Probabilistic Modelling is Sufficient for Causal Inference

What this episode covers

This position paper argues that probabilistic modeling is sufficient for causal inference, directly challenging the prevalent idea that specialized causal frameworks or notation, such as Pearl's "do-operator," are necessary. The authors demonstrate through concrete examples like aspirin's effect on headaches how interventional and counterfactual questions can be answered by explicitly defining joint probability distributions across observed and hypothetical "intervened" or "counterfactual" worlds. They present the "twin model approach" using Bayesian Networks to illustrate this, emphasizing that conventional causal tools can be reinterpreted as convenient "syntactic sugar" within a broader probabilistic framework. Ultimately, the paper advocates for a more accessible and flexible approach to causal problems within the machine learning community, asserting that the perceived "causal-statistical dichotomy" is primarily a semantic issue.

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