I'm developing a forecasting application whose purpose is to allow an importer to forecast demand for its products from its customer network of distributors. Sales figures are a pretty good proxy for demand, so long as there is adequate inventory to fill the demand. When inventory gets drawn down to zero, though (the situation we're looking to help our customer avoid), we don't know much much we missed the target by. How many sales would the customer have made, had they had sufficient supply? Standard regression-based ML approaches that use Sales as a simple target variable will produce inconsistent estimates of the relationship between time, my descriptive variables, and demand.
Tobit modeling is the most obvious way to approach the problem: http://en.wikipedia.org/wiki/Tobit_model. I am wondering about ML adaptations of random forests, GBMS, SVMs, and neural networks that also account for a left-handed censored structure of the data.
In short, how do I apply machine learning tools to left-censored regression data to get consistent estimates of the relationships between my dependent and independent variables? First preference would be for solutions available in R, followed by Python.