Regression-assisted inference for the average treatment effect in paired experiments

DOI10.1093/BIOMET/ASY034zbMath1506.62433arXiv1612.05179OpenAlexW2963783520WikidataQ129764945 ScholiaQ129764945MaRDI QIDQ4562745

Colin B. Fogarty

Publication date: 18 December 2018

Published in: Biometrika (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1612.05179

zbMATH Keywords

causal inferenceaverage treatment effectcovariance adjustmentfinite-sample inferencepaired experimentsagnostic regression

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Linear inference, regression (62J99) Causal inference from observational studies (62D20)

Related Items (9)

Randomization Tests for Weak Null Hypotheses in Randomized Experiments ⋮ Reconciling design-based and model-based causal inferences for split-plot experiments ⋮ Detecting heterogeneous treatment effects with instrumental variables and application to the Oregon Health Insurance Experiment ⋮ On randomization-based causal inference for matched-pair factorial designs ⋮ The Generalized Oaxaca-Blinder Estimator ⋮ Inference in Experiments With Matched Pairs ⋮ Regression-adjusted estimation of quantile treatment effects under covariate-adaptive randomizations ⋮ Covariate-adjusted Fisher randomization tests for the average treatment effect ⋮ Decomposing Treatment Effect Variation

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