Is regression adjustment supported by the Neyman model for causal inference?
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Publication:1036725
DOI10.1016/j.jspi.2009.07.008zbMath1178.62079OpenAlexW2081407256MaRDI QIDQ1036725
Publication date: 13 November 2009
Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jspi.2009.07.008
experimental designsaverage treatment effectsregression adjustmentNeyman causal modelsocial policy interventions
Applications of statistics to social sciences (62P25) Linear inference, regression (62J99) Sampling theory, sample surveys (62D05) Foundations and philosophical topics in statistics (62A01)
Related Items (7)
On randomization-based and regression-based inferences for \(2^K\) factorial designs ⋮ Reconciling design-based and model-based causal inferences for split-plot experiments ⋮ The Generalized Oaxaca-Blinder Estimator ⋮ Design-Based Ratio Estimators and Central Limit Theorems for Clustered, Blocked RCTs ⋮ A unified analysis of regression adjustment in randomized experiments ⋮ Agnostic notes on regression adjustments to experimental data: reexamining Freedman's critique ⋮ A paradox from randomization-based causal inference
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