The aim of this paper is to compare the performance of the conditional nonparametric approach with several traditional nonparametric methods to incorporate the effect of exogenous or environmental variables into the estimation of efficiency measures. To do this, we conduct a Monte Carlo experiment using a translog production function with one output, two discretionary inputs and two exogenous variables to generate simulated data. According to the values of different accuracy measures calculated to evaluate the performance of each method, the conditional data envelopment analysis clearly outperforms all the traditional alternatives.