Arisa Sadeghpour | Searcharxiv

arXiv2026

Outcome Modeling in Design-Based Inference for Spatial Settings

In the face of spatial interference, researchers are often interested in estimating treatment effects at specific points located in space. Wang et al. (2025) and Pollmann (2023) provide design-based frameworks for estimating spillover effects on points located across a range of distances from interventions. Although these frameworks are design-based, we show their proposed estimands rely on outcomes that are directly unobservable and therefore, require outcome modeling. When using modeled outcomes in practice, even the typically design-unbiased Horvitz-Thompson estimator can accrue bias as a result of the modeling error. The performance of spatial outcome models depends on the density or resolution of observed outcomes. Through simulation, we find that the bias of the estimators decays with increasing outcome density, but not with increasing numbers of intervention units, and standard errors using modeled outcomes converge to the oracle standard errors. To demonstrate the role of outcome modeling with spatial interference, we reanalyze an experiment from Collazos et al. (2021) on the effect of hot spots policing on crime and provide several suggestions for practice.

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arXiv2025

Inference with weights: Residualization produces short, valid intervals for varying estimands and varying resampling processes

Weighting procedures are used in observational causal inference to adjust for covariate imbalance within the sample. Common practice for inference is to estimate robust standard errors from a weighted regression of outcome on treatment. However, it is well known that weighting can inflate variance estimates, sometimes significantly, leading to standard errors and confidence intervals that are overly conservative. We instead examine and recommend the use of robust standard errors from a weighted regression that additionally includes the balancing covariates and their interactions with treatment. We show that these standard errors are more precise and asymptotically correct for weights that achieve exact balance under multiple common resampling frameworks, including design-based and model-based inference, as well as superpopulation sampling with a finite sample correction. Gains to precision can be quite significant when the balancing weights adjust for prognostic covariates. For procedures that balance only approximately or in expectation, such as inverse propensity weighting or approximate balancing weights, our proposed method improves precision by reducing residuals through augmentation with the parametric model. We demonstrate our approach through simulation and re-analysis of multiple empirical studies.

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