The hard part is defining "why". Machine learning methods can produce a model which fits the data very well, and you can easily prove that it fits the data, but understanding "why" is much harder.
There is a tool called Eureqa which was specifically designed to produce understandable models, in the form of mathematical equations. A biologist used it on some data from an experiment of his, and it produced a very simple equation that fit the data perfectly. But he couldn't publish it because be couldn't understand or explained why the equation worked or what it meant.
There is a tool called Eureqa which was specifically designed to produce understandable models, in the form of mathematical equations. A biologist used it on some data from an experiment of his, and it produced a very simple equation that fit the data perfectly. But he couldn't publish it because be couldn't understand or explained why the equation worked or what it meant.