Efficiency Study of Estimators for a Treatment Effect in a Pretest–Posttest Trial

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Models for Value-Added Modeling of Teacher Effects (2004, background): “Yang and Tsiatis (2001) consider relative efficiency in a semiparametric model assuming only the first two moments of the joint distribution.”
Is regression adjustment supported by the Neyman model for causal inference (2007, methodology): “Another contribution is that it generalizes the asymptotic results in Freedman (2006) and Yang and Tsiatis (2001) to include multiple covariates (that are typically used to estimate regression-adjusted ATEs) rather than a single covariate.”
Agnostic notes on regression adjustments to experimental data: Reexamining Freedman's critique (2012, result): “Similar results on the asymptotic precision of OLS adjustment with interactions are proved in interesting and useful papers by Yang and Tsiatis (2001), Tsiatis et al. (2008), and Schochet (2010), under the assumption that the subjects are a random sample from an infinite superpopulation.2 These…”
The Generalized Oaxaca-Blinder Estimator (2020, background): “Unlike earlier work by Yang & Tsiatis (Yang and Tsiatis, 2001), Lin’s proof does not use any probabilistic assumptions other than that the treatment assignments Zi are assigned completely at random.”
Revisiting regression adjustment in experiments with heterogeneous treatment effects (2020, methodology): “(38) By Wooldridge (2010) question 12.17, this converges in probability to (37).”