A causal edge claims that intervening on one variable changes another under stated assumptions. Correlation or sequence alone does not justify that edge.

Causal Graphs Assert More Than Association

A Bayesian network factors a joint probability using conditional dependencies in a directed acyclic graph. Evidence updates beliefs, but the result inherits model structure and probability assumptions.

Bayesian Networks Update Structured Uncertainty

Algorithms can infer candidate causal structures from observational or interventional data, but equivalence classes, hidden confounders, and sampling choices limit conclusions.

Causal Discovery Produces Hypotheses Under Assumptions

A counterfactual test changes one relevant or irrelevant feature and observes whether routing changes appropriately. It can reveal brittle shortcuts and unfair proxy use.

Counterfactual Tests Probe Decision Sensitivity

In this situation—deciding whether tutor hints caused higher mastery—which choice best applies “Causal Graphs Assert More Than Association”?

A world model predicts how environment state may change after actions. Planning against it can reduce real-world trials, but model error compounds along imagined trajectories.

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