What a causal model is, and what it answers.

Causal Models → A causal model doesn't predict. It simulates.

Prediction is a Rung 1 operation. Simulation is a Rung 2 operation. The distinction is not technical — it is logical. A tool that predicts describes the world as it is. A tool that simulates reasons about the world as it would be under a specific intervention. Those are different questions that require different formal structures.

Bayesian Networks → A Bayesian network is a map of what causes what.

Not a statistical summary of what has happened. A formal representation of the mechanism — which variables cause which others, with what strength, under what conditions. The map can be queried in any direction: forward to predict consequences, backward to diagnose causes.

Structural Causal Models → An SCM is a machine for generating the world.

A Bayesian network is a joint distribution with causal direction. A Structural Causal Model is the formal object that supplies what the Bayesian network leaves implicit — the individual-level structural equations, and the exogenous variables that make Rung 3 (counterfactual) reasoning computable rather than rhetorical.

Updating with New Data → Every piece of new evidence should change your beliefs. Bayesian updating tells you by exactly how much.

Organizations accumulate evidence continuously — operational data, market signals, audit findings, incident reports. Most of this evidence is used informally, if at all. Bayesian updating is the mechanism for using it formally: starting from a prior belief, incorporating the evidence, and arriving at a posterior belief that is the correct basis for the next decision.

Pearl's Ladder → There are three questions a model can answer. Most answer only one.

Pearl’s Ladder is a logical hierarchy, not a technical one. Three question types are categorically distinct kinds of reasoning — not levels of sophistication. This page covers the formal proof that the gap between them cannot be closed by scale, data, or algorithmic improvement.

Rung 3 Procedure → The Three-Step Procedure in practice

Every population model can tell you what tends to happen. Only a counterfactual model can tell you what would have happened to this specific case under different conditions — because it anchors the individual's unobserved background from what actually happened before running the hypothetical. Select a domain below and run all three steps on a real causal model.

Capabilities → Pearl’s ladder is the headline. The rest of the menu is wider.

Once a causal model exists, an unexpectedly wide set of substantive questions reduce to inferential procedures on the model — mediation, sensitivity to unmeasured confounding, transportability, selection-bias correction, attribution, dynamic treatment regimes, fairness, mechanism interventions, distribution-shift robustness. This page is the menu of what the framework can be asked.