Causal Models and Interventions › Causal Models and Interventions

A building-management agent watches months of logs from a climate system. On average in the logs, higher cooling is associated with a temperature drop smaller than the engineers' physics model predicts. Operators turn cooling up mostly on hot days, so strong cooling is systematically paired with strong heat loads. If the agent interprets that association as the response to an intervention, it learns the wrong slope and can choose the wrong plan.

Notice how subtle this failure is. The log is enormous by the standards of this example: months of data and thousands of episodes. A regression can still be the best linear predictor available within the distribution the operators generated while explaining only part of the outcome variance. Ordinary held-out error from the same logging policy does not test the quantity the planner needs: the response to a deliberately changed action. The failure appears when the model is used, especially at action values and in situations that the operators' behavior policy rarely paired together. This separates predictive fit under one policy from decision-making validity under another; a predictive evaluation can reveal weak fit, but it cannot by itself certify an interventional effect.

This is the central problem of the chapter. Models trained only for observational prediction learn relations under the data-collection process they experienced. Planning, by contrast, evaluates deliberately selected actions, which can be represented as interventions on an action node. A predictor may generalize across a policy change when its learned relation and support remain valid, or fail when they do not. Correctly specified and identified causal mechanisms provide semantics for such changed-policy queries; writing down a causal graph alone does not remove misspecification, nonidentifiability, or limited support.

The word "mechanism" is doing heavy lifting here, so it is worth pausing on what it buys us. A joint distribution determines probabilities of observational events (or densities over regions for continuous variables), but it says nothing by itself about which outcomes would arise if you reached in and changed something. A set of mechanisms, by contrast, is compositional. Each rule has its own identity, inputs, and domain of validity, and knowing the rules separately can support reasoning about swapping one out while leaving the others in place. This is useful for agents because each action replaces the default rule for what action happens next with the agent's choice. Those intervention semantics are additional structure; they are not recoverable from an observational joint distribution alone.

In this chapter we build the causal vocabulary step by step: structural causal models, the distinction between seeing and doing, counterfactual reasoning, confounding and adjustment, and the properties of invariance and transportability that make causal knowledge reusable. We close with causal representation learning, the open research problem of getting causal structure out of raw sensors, which connects directly to the latent world models of Part III.