Spatial Interference

Spatial interference, the phenomenon where a treatment's effect at one location influences outcomes at other locations, poses significant challenges for causal inference, particularly in spatiotemporal settings. Current research focuses on developing robust methods to estimate treatment effects in the presence of this interference, employing techniques like deep learning models (including U-Net architectures and deepsets) and leveraging clustered experimental designs to mitigate bias. These advancements aim to improve the accuracy of causal estimations in various fields, such as policy evaluation and environmental studies, where spatial dependencies are prevalent, leading to more reliable insights from observational and experimental data.