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Statistical Models and Causal Inference by Jasjeet S. Sekhon, David Collier, David A. Freedman

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12On Regression Adjustments in Experiments with Several Treatments

        ABSTRACT. Regression adjustments are often made to experimental data to address confounders that may not be balanced by randomization. Since randomization does not justify the models, bias is likely; nor are the usual variance calculations to be trusted. Here, we evaluate regression adjustments using Neyman’s non-parametric model. Previous results are generalized, and more intuitive proofs are given. A bias term is isolated, and conditions are given for unbiased estimation in finite samples.

12.1  Introduction

        Data from randomized controlled experiments (including clinical trials) are often analyzed using regression models and the like. The behavior of the estimates ...

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