Article summary
Conjoint analysis measures multidimensional preferences by estimating the average marginal component effect (AMCE) — the causal effect of a single profile attribute averaged over the other attributes. The AMCE, however, depends critically on the distribution used for that averaging. Most experiments default to a uniform distribution that weights every profile equally, but real-world profile frequencies and theoretically relevant counterfactual distributions are often far from uniform. This mismatch can seriously undermine the external validity of conjoint findings.