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A unified framework for estimating direct causal effect under spatial confounding and interference, with the R package spaci
Estimating causal effects is challenging with observational data due to the lack of exchangeability, and is further complicated with spatial data. In real-life applications such as agricultural and environmental studies, unobserved spatial factors (spatial confounding, SC) and interactions of units
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