The authors introduce a latent factor panel model for partial identification of causal effects in spatiotemporal data under a factor confounding assumption, achieving point identification with limited interference assumptions.
Spatial causal inference in the presence of unmeasured confounding and interference
2 Pith papers cite this work. Polarity classification is still indexing.
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A hierarchical model with bivariate GEV distributions and intrinsic CAR spatial structure attributes causal effects of anthropogenic forcing on annual temperature maxima return levels using CMIP6 factual and counterfactual simulations.
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A Latent Factor Panel Approach to Spatiotemporal Causal Inference
The authors introduce a latent factor panel model for partial identification of causal effects in spatiotemporal data under a factor confounding assumption, achieving point identification with limited interference assumptions.
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Estimating Causal Attribution of Anthropogenic Forcing on High-Temperature Extremes Using a Latent Gaussian Spatial Model
A hierarchical model with bivariate GEV distributions and intrinsic CAR spatial structure attributes causal effects of anthropogenic forcing on annual temperature maxima return levels using CMIP6 factual and counterfactual simulations.