I really don't know any machine learning, but have a problem that seems like one where I should use some ML algorithm.
I am analyzing a medical study with one age-related condition, age, a treatment, gender, and the abundance of two particular gut bacteria. Many researchers in the field also like to look at the ratio of these two bacteria.
Playing around with some regressions with one, two, or three explanatory variables, I found some unexpected combinations with very good p-values. For instance, controlling for age, bacteria-A seems to be strongly associated with the condition regardless of treatment. The other bacteria seems to be strongly associated with the treatment regardless of age. I would have had no way to expect this to be the case.
I feel there might be value in searching for more unexpected associations. I can make a list of all one, two, and three combination explanatory variables and perform regression of my six variables against these combinations and basically sort by p-value. But, 1) this sort of p-value mining is generally frowned upon, and 2) there are a bazillion possible regressions.
Seems like there is probably some sort of ML algorithm that would hunt down the unexpected associations in an objective and systematic way.
What would that be?