  A measure of relative importance of variables is often desired by researchers
when the explanatory aspects of econometric methods are of interest. To this
end, the author briefly reviews the limitations of conventional econometrics in
constructing a reliable measure of variable importance. The author highlights
the relative stature of explanatory and predictive analysis in economics and
the emergence of fruitful collaborations between econometrics and computer
science. Learning lessons from both, the author proposes a hybrid approach
based on conventional econometrics and advanced machine learning (ML)
algorithms, which are otherwise, used in predictive analytics. The purpose of
this article is two-fold, to propose a hybrid approach to assess relative
importance and demonstrate its applicability in addressing policy priority
issues with an example of food inflation in India, followed by a broader aim to
introduce the possibility of conflation of ML and conventional econometrics to
an audience of researchers in economics and social sciences, in general.
