  After performing a randomized experiment, researchers often use
ordinary-least squares (OLS) regression to adjust for baseline covariates when
estimating the average treatment effect. It is widely known that the resulting
confidence interval is valid even if the linear model is misspecified. In this
paper, we generalize that conclusion to covariate adjustment with nonlinear
models. We introduce an intuitive way to use any "simple" nonlinear model to
construct a covariate-adjusted confidence interval for the average treatment
effect. The confidence interval derives its validity from randomization alone,
and when nonlinear models fit the data better than linear models, it is
narrower than the usual interval from OLS adjustment.
