DML-Geo: an ensemble double machine learning framework for estimating spatially heterogeneous causal effects

Causal inference in geographical sciences faces the challenge of isolating treatment effects from high-dimensional observational data, complicated by spatial non-stationarity and persistent confounding. Double machine learning (DML) offers a powerful solution for high-dimensional debiasing through orthogonalization and cross-fitting, but traditional variants overlook spatial heterogeneity by treating space as a simple covariate. To address this, we introduce DML-Geo, an ensemble extension of DML for estimating spatially varying causal effects. Retaining the orthogonalization procedure of DML at its first stage, DML-Geo augments the second stage with three complementary estimators, namely a linear regression model for covariate-driven effects, a generalized additive model (GAM) for spatially smoothed additive effects, and geographically weighted regression (GWR) for localized patterns. Robustness is further enhanced by an adaptive weighting scheme based on inter-model correlations to aggregate outputs from these variants, complemented by a bootstrap procedure for significance testing. Extensive simulations confirm DML-Geo’s superior precision and stability relative to its component models and competing baselines. In real-world applications to housing prices and mental health outcomes, DML-Geo uncovers interpretable spatial causal effect patterns, offering place-specific insights to support policy decisions. DML-Geo provides a flexible toolkit for geospatial causal inference that does not require causal graphs or strong structural assumptions.