Develops novel bounds on average treatment effects by pooling limited information across observations for robustness under unconfoundedness, with inference methods.
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Develops design-based causal inference methods for spatial treatments using counterfactual candidate locations, extends double ML for spatial correlations, and applies to grocery store effects on foot traffic.
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Bounding Treatment Effects by Pooling Limited Information across Observations
Develops novel bounds on average treatment effects by pooling limited information across observations for robustness under unconfoundedness, with inference methods.
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Causal Inference for Spatial Treatments
Develops design-based causal inference methods for spatial treatments using counterfactual candidate locations, extends double ML for spatial correlations, and applies to grocery store effects on foot traffic.