Standard regression output suggests which covariates are significant and similarly standard random forest output suggests which are relatively important for the response.
We might be able to see the important relationships visually, but for large N or high dimensional data, how can we determine the set of covariates that has had the maximum impact on the response for a given observation? For example, if $Y = x_1 + x_2 + X_3$, then $Y$ can equal $2$ for many combinations of $x_1$, $x_2$ and $x_3$ values.
By comparison, if we build a CART tree then we can traverse the tree from the bottom up to get the cause of various covariates on the response. But it is not possible for random forest or regression. Are there any analytical methods to assess this?
I am providing practical example – Everyday “stats.stackexchange” gets many requests and based many parameters (IVs – count of SME available for that particular subject, backlog in that queue, weekday/holiday,…) the time to response (DV – assume - 1 hour after question post) are calculated/predicted.
On any particular day, the time to response is 5 hours (say 5 times of expected mean time of response) then question comes why today response time is 5 hours and what factors (IVs) has contributed. This is what, I am trying to understand