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Causality in spatial economic analysis: with reference to the London Green Belt and house prices

Bernard Fingleton

Spatial Economic Analysis2026https://doi.org/10.1080/17421772.2025.2603503article
AJG 2ABDC B
Weight
0.50

What the paper says

This paper illustrates possibilities and pitfalls of two different approaches to causal analysis, the Rubin causal framework (RCF) and spatial econometrics, namely the spatial Durbin model (SDM). The two are directly opposing, for unlike the RCF, spatial interaction is embodied within the SDM. The main innovatory aspects are an attempt to reconcile RCF and SDM via ATIVE, meaning the average treatment instrumental variable effect, which parallels the average treatment effect (ATE) based on regression adjustment (RA). Additionally the paper illustrates the efficacy of using synthetic instrumental variables, providing estimates of the impact of London’s Green Belt on house prices.

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https://doi.org/https://doi.org/10.1080/17421772.2025.2603503

Or copy a formatted citation

@article{bernard2026,
  title        = {{Causality in spatial economic analysis: with reference to the London Green Belt and house prices}},
  author       = {Bernard Fingleton},
  journal      = {Spatial Economic Analysis},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1080/17421772.2025.2603503},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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