This news article form the Guardian:
Biofuels have forced global food prices up by 75% - far more than previously estimated - according to a confidential World Bank report obtained by the Guardian.
The damning unpublished assessment is based on the most detailed analysis of the crisis so far, carried out by an internationally-respected economist at global financial body. ...
It will also put pressure on the British government, which is due to release its own report on the impact of biofuels, the Gallagher Report. The Guardian has previously reported that the British study will state that plant fuels have played a "significant" part in pushing up food prices to record levels. Although it was expected last week, the report has still not been released.
Without seeing the study it is hard to determine how the 75% figure was arrived at but here is a speculation.
1. It was done using an analysis of variance so that 75 percent of the variation in the changes in food prices is caused by some variable that measures the change in the amount of biofuels produced.
2. If this is the case, then I don't really consider this causality per se but the finding is interesting nonetheless. Indeed if this is the case then I would not say that biofuels have caused food prices to go up by 75 percent.
3. It could have been done using the coefficient in a standardized regression although standardized regressions are criticized because some predictors are easier to change than others regardless of the unit of measurement. More recent discussion especially between Sander Greenland and Andrew Gelman can be found here. *
4. The results could be sensitive to the variable used to proxy for amount of biofuels produced - it could be amount of crops diverted or change in amount of bioenergy or something else although I would expect that some sensitivity analysis would have been done and perhaps the press merely used the largest number for publicity purposes.
* Sander Greenland is coauthor of "The fallacy of employing standardized regression coefficients and correlations as measures of effect" (1986) in American Journal of Epidemiology and his critique is quite harsh.