Neural-network spatial regression with an approximate Gaussian process estimates direct, indirect, and total causal effects for continuous treatments under spatial interference and unobserved confounding, and outperforms linear spatial regression in the paper's experiments.
Counterfactual representation learning with balancing weights
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Deep Causal Inference for Point-referenced Spatial Data with Continuous Treatments
Neural-network spatial regression with an approximate Gaussian process estimates direct, indirect, and total causal effects for continuous treatments under spatial interference and unobserved confounding, and outperforms linear spatial regression in the paper's experiments.