Contemporary Tabular Data Generators Do Not Preserve Conceptual Structure

Synthetic tabular data is usually judged by agreement with low-order marginals, by the loss of a downstream predictor, or by the failure of membership-inference attacks. Each is computed from a low-dimensional projection of the joint distribution, so none can tell whether a generator preserved the dependence structure among features. We measure this conceptual structure by treating a binary table as a formal context. We score 15 generators over multiple seeds, 13 learned and two structure-free baselines, on 63 real-world contexts. Each generated table is compared with its ground truth at matched density on the number of closed attribute sets, the size of the canonical implication base, and two bounded rise rates of the concept lattice. All 13 learned generators produce synthetic data with fewer closed sets and a smaller canonical base than their ground truth, and on these counts none is measurably better than a structure-free baseline. The two rise rates order the generators differently, and on them some learned generators are more faithful than one of the two baselines, so the second result holds in full for the counts only.