Causal Models and Interventions › Limitations and Impact

It would be easy to leave this chapter with the impression that the fix is always "adjust for the confounder" or "run the randomized experiment." Adjustment requires observing a valid set with support; in partially observed settings, relevant common causes may be unavailable. Conditioning on a collider or its descendant can instead induce selection bias, including when outcome-based filtering selects through such a variable. Randomization can be expensive or unsafe and identifies only the assignments it randomizes with adequate support. Counterfactual queries make stronger structural commitments than interventional ones. In high-dimensional neural settings, both discovery and representation-learning guarantees remain method- and assumption-specific, with computational and statistical difficulty depending on the graph, model class, interventions, and observation process.

To these structural limits, add a practical one about the granularity of mechanisms. The theory is silent on where to place the module boundaries: whether contact physics is one mechanism or a family of per-object mechanisms, whether a user's preference is one variable or a time-varying hierarchy of them. Different granularities support different interventions and different transportability conclusions, and choosing among them is a modeling judgment that the formalism constrains but does not determine. This is the same tradeoff space we faced in building state abstractions and latent models, and the honest summary is that causal thinking sharpens the questions without mechanizing the answers.

The causal viewpoint changes a model's semantics, not necessarily its software shape. Predictive and causal models can both be modular neural systems; the causal model additionally states how variables and mechanisms respond to interventions. When changed mechanisms are localized, autonomous, and identifiable, that structure can justify selective re-estimation. Hidden confounding is one possible reason offline dynamics fail under new policies, while limited support and approximation error are others. Interventional data, valid adjustment, invariance across informative environments, and robust bounds are possible tools rather than an exhaustive cure list.

It also separates four useful notions of quality. Predictive fidelity describes fit to a specified distribution. Disentangled representations can make intervention targets interpretable, though an invertible entangled state plus a correct action-conditioned model may still support some causal queries. Uncertainty quality is relative to a query distribution, and in-distribution calibration does not establish interventional calibration. Decision usefulness depends on the task: causal structure is especially valuable when planning changes the data-generating process, but useful decisions do not logically require an explicit causal representation in every setting.

For evaluation, test the model under the queries it will receive: held-out prediction under the logging distribution, randomized or scripted actions that test interventional validity, and controlled mechanism changes that test localized adaptation. A discrepancy under intervention may reflect confounding, limited support, or approximation error, so the evaluation should be designed to separate them. Counterfactual probes are useful only where a simulator or other trusted construction supplies the same-unit ground truth.