Structural coefficients from the fitted Gaussian CLG. Rows = cause, columns = effect. Blue = positive weight, red = negative. U nodes excluded. Weights are fitted parameters, not elicited estimates.
The weight matrix is the skeleton of the structural causal model — the fitted linear coefficients that quantify how strongly each direct cause acts on each effect. Unlike correlation coefficients, these weights have a directional, mechanistic interpretation: holding all other parents fixed, a weight of +1.5 means one unit of cause produces 1.5 units of effect downstream, not merely that the two tend to move together.
The matrix makes model transparency concrete. Every quantitative claim — path effects, sensitivity scores, counterfactual predictions — is a composition of these weights across chains of edges. Inspecting the matrix is the first check that domain knowledge was correctly encoded and that the fitted model behaves as intended.