  In randomized experiments, linear regression with baseline features can be
used to form an estimate of the sample average treatment effect that is
asymptotically no less efficient than the treated-minus-control difference in
means. Randomization alone provides this "do-no-harm" property, with neither
truth of a linear model nor a generative model for the outcomes being required.
We present a general calibration step which confers the same no-harm property
onto estimators leveraging a broad class of nonlinear models. The process
recovers the usual regression-adjusted estimator when ordinary least squares is
used, and further provides non-inferior treatment effect estimators using
methods such as logistic and Poisson regression. The resulting estimators are
non-inferior with respect to both the difference in means estimator and with
respect to treatment effect estimators that have not undergone calibration.
