It is well known that efficient estimation of average treatment effects can be obtained by the method of inverse propensity score weighting, using the estimated propensity score, even when the true one is known. When the true propensity score is unknown but parametric, it is conjectured from the literature that we still need nonparametric propensity score estimation to achieve the efficiency. We formalize this argument and further identify the source of the efficiency loss arising from parametric estimation of the propensity score. We also provide an intuition of why this overfitting is necessary. Our finding suggests that, even when we know that the true propensity score belongs to a parametric class, we still need to estimate the propensity score by a nonparametric method in applications.
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