A unified least squares framework for identifying and estimating causal effects in crossover designs that remains valid under misspecified working models.
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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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Principled analysis of crossover designs: causal effects, efficient estimation, and robust inference
A unified least squares framework for identifying and estimating causal effects in crossover designs that remains valid under misspecified working models.
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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.