Stochastic Boundaries in Spatial General Equilibrium: A Diffusion-Based Approach to Causal Inference with Spillover Effects
Tatsuru Kikuchi

TL;DR
This paper presents a diffusion-based framework for causal inference in spatial economics, identifying when local interventions lead to systemic effects through stochastic boundary detection, with applications to AI adoption in Japan.
Contribution
It introduces a novel diffusion-based approach combining boundary detection and jump-diffusion modeling to identify regime shifts in spatial spillovers.
Findings
Treatment effects jump at ~35km spatial scale.
General equilibrium effects amplify partial estimates by 42%.
Ignoring boundaries underestimates effects by 28-67%.
Abstract
This paper introduces a novel framework for causal inference in spatial economics that explicitly models the stochastic transition from partial to general equilibrium effects. We develop a Denoising Diffusion Probabilistic Model (DDPM) integrated with boundary detection methods from stochastic process theory to identify when and how treatment effects propagate beyond local markets. Our approach treats the evolution of spatial spillovers as a L\'evy process with jump-diffusion dynamics, where the first passage time to critical thresholds indicates regime shifts from partial to general equilibrium. Using CUSUM-based sequential detection, we identify the spatial and temporal boundaries at which local interventions become systemic. Applied to AI adoption across Japanese prefectures, we find that treatment effects exhibit L\'evy jumps at approximately 35km spatial scales, with general…
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Taxonomy
TopicsSpatial and Panel Data Analysis · Regional Economics and Spatial Analysis · Advanced Causal Inference Techniques
