We model the energy–agriculture linkage through structural vector autoregression (VAR) model. This model quantifies the relative importance of various contributing factors in driving prices in both markets. The LiNGAM algorithm from the machine learning literature is used to help identify structural parameters in contemporaneous time. We perform conditional forecasting, taking into account the renewable fuel standards policies, and compare the forecasted path of prices with and without the renewable fuels mandates.