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Partial mean with generated regressors arises in several econometricproblems, such as the distribution of potential outcomes with continuoustreatments and the quantile structural function in a nonseparable triangularmodel. This paper proposes a nonparametric estimator for the partial meanprocess, where the second step consists of a kernel regression on regressorsthat are estimated in the first step. The main contribution is a uniformexpansion that characterizes in detail how the estimation error associated withthe generated regressor affects the limiting distribution of the marginalintegration estimator. The general results are illustrated with two examples:the generalized propensity score for a continuous treatment (Hirano and Imbens,2004) and control variables in triangular models (Newey, Powell, and Vella,1999; Imbens and Newey, 2009). An empirical application to the Job Corpsprogram evaluation demonstrates the usefulness of the method.

Partial Mean Processes with Generated Regressors: Continuous Treatment Effects and Nonseparable Models