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20162025
most citedInference at the data's edge: Gaussian processes for modeling and inference under model-dependency, poor overlap, and extrapolation

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stat.ME2024★ 1 cited

Inference at the data's edge: Gaussian processes for modeling and inference under model-dependency, poor overlap, and extrapolation

Soonhong Cho, Doeun Kim, Chad Hazlett

Many inferential tasks involve fitting models to observed data and predicting outcomes at new covariate values, requiring interpolation or extrapolation. Conventional methods selec…

stat.ME2024

Demystifying and avoiding the OLS "weighting problem": Unmodeled heterogeneity and straightforward solutions

Tanvi Shinkre, Chad Hazlett

Researchers frequently estimate treatment effects by regressing outcomes (Y) on treatment (D) and covariates (X). Even without unobserved confounding, the coefficient on D yields a…