Abstract: In an example embodiment, a machine-learned model is trained to forecast a performance-based metric for a piece of content based on a budget applied to the piece of content. The increase in the performance-based metric that is due to a corresponding increase in budget may be termed “incrementality.” The machine-learned model is trained in such a way that incrementality is built into the model. More particularly, in an example embodiment, an asymmetric budget split process is used to create two groups of training data, one for high budget and one for low budget. Rather than relying on historical data, the asymmetric budget split process applies a high budget to a piece of content in a first subgroup (e.g., group of users) and a low budget to that same piece of content in a second subgroup, and then the performance results in each subgroup are compared.