Control for overlap between input covariates and evaluation targets

Establish how much the reported cross-model performance differences are attributable to overlap between PlacePulse input covariates and downstream evaluation targets by conducting a controlled comparison that excludes overlapping inputs.

Background

Several downstream targets share covariates with the structural attributes used to train PlacePulse, and some PDFM comparisons may involve analogous input–target overlap. The paper notes that such overlap can make performance reflect preservation of shared covariates rather than representation quality alone.

A controlled evaluation excluding overlapping inputs is not reported, leaving unresolved the extent to which the observed advantages of PlacePulse, especially on health and economic outcomes, result from genuine transferable representation quality versus shared information between training inputs and evaluation targets.

References

Fifth, some downstream targets share covariates with the inputs of PlacePulse, and in some cases PDFM (\xref{Table}{tab:targets}). Several were included deliberately, to check whether the embeddings preserve the socio-economic and health gradients present in the inputs (\xref{Section}{sec:targets}), but cross-model differences on these targets may partly reflect input-target overlap rather than representation quality alone; a controlled comparison excluding the overlapping inputs is left for future work.

— PlacePulse: Learning Transferable Place Representations from Structural Geographic Data  (2610.13390 - García-Duarte et al., 9 Oct 2026) in Section 6.3, Discussion, subsection “Implications for practice and responsible use,” and Section 6.4, Limitations