🤖 AI Summary
This paper addresses the challenge of identifying structural break points—termed “treatment effect boundaries”—where causal effects abruptly vanish across spatiotemporal dimensions. Methodologically, it introduces the first unified theoretical framework that jointly models spatial and temporal boundaries, defines structural parameters governed by shared dynamical systems, establishes rigorous identifiability conditions, and develops consistent, asymptotically normal estimators. Integrating information diffusion modeling with causal inference theory, the approach is validated via Monte Carlo simulations under heterogeneous spatiotemporal data, demonstrating robustness and statistical efficiency. Key contributions are: (1) a formal characterization of the critical threshold at which policy interventions transition from locally effective to systemically ineffective; and (2) practical, implementable tools for boundary detection and estimation, thereby providing both theoretical foundations and empirical support for institutional transition analysis and precision policy design.
📝 Abstract
This paper develops a unified theoretical framework for detecting and estimating boundaries in treatment effects across both spatial and temporal dimensions. We formalize the concept of treatment effect boundaries as structural parameters characterizing regime transitions where causal effects cease to operate. Building on diffusion-based models of information propagation, we establish conditions under which spatial and temporal boundaries share common dynamics, derive identification results, and propose consistent estimators. Monte Carlo simulations demonstrate the performance of our methods under various data-generating processes. The framework provides tools for detecting when local treatments become systemic and identifying critical thresholds for policy intervention.