The continuous expansion of digital infrastructure, especially data centers, has imposed unprecedented, geographically concentrated stress on existing power grids. However, rigorous causal evidence quantifying how these facilities reshape local power-infrastructure supply remains limited. This study builds a comprehensive county-level US panel dataset spanning 2010-2024, uniquely integrating individual data center building footprints and power-plant additions. To mitigate endogeneity and high-dimensional confounding, this study deploys Double Machine Learning with five-fold cross-fitting, conditioning on county and year fixed effects alongside regional socioeconomic covariates. Baseline estimation identifies a significant positive causal effect robust across multiple rigorous specifications. The result holds following 1%-99% winsorization that downweights mega-clusters including Loudoun County: growth in data-center floor area drives meaningful increases in nearby power-generation capacity (ATE=1.982, P=0.000, CI: [1.25, 2.72]). Outcomes remain statistically significant using a one-year lagged treatment to address reverse causality, and are stable under state-level fixed-effect alternatives. An event-study framework augmented with historical agglomeration controls validates clear parallel trends and detects a sharp causal spike at t = 3, matching typical utility-scale grid-construction lead times. A placebo test further verifies these causal estimates cannot emerge from random chance. Substantial regional heterogeneity emerges: strong positive impacts concentrate in the West, while significant negative displacement-related effects appear within resource-constrained Northeastern counties. These results deliver timely insights for urban planners and energy regulators, stressing the importance of forward-looking grid-capacity investment to support ongoing digital transformation.

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