Properties of least squares estimator in estimation of average treatment effects

Treatment effects are often estimated by the least squares estimator controlling for some covariates. This paper investigates its properties. When the propensity score is constant, it is a consistent estimator of the average treatment effects if it is viewed as a semiparametric partially linear regression estimator, but it is not necessarily more efficient than the simple difference-of-means estimator. If it is literally viewed as a least squares estimator with a finite number of controls, it is equal to the weighted average of conditional average treatment effects with potentially negative weights, although the negative weight issue does not exist under semiparametric interpretation. It is shown that the negative weight issue can be avoided by use of logit specification.