This may be a dumb question. I'm a recent college grad who is working in the area of predictive modeling and finding that there is a heavy emphasis on performing feature engineering. However, in most of my academic training in statistics, there was almost no mention of feature engineering and the like (besides arguments against discretizing/binning predictors) for the purpose of building inferential models. I was wondering why feature engineering plays a bigger role when doing predictive modeling as opposed to developing models for statistical inference. So...what is the role of feature engineering in statistical inference? (as opposed to the role of feature engineering in predictive modeling)
Based on the recent comment:
By statistical inference, I mean any analysis where the main goal is to assess the relationship between a predictor and response variable.
By predictive modeling, I mean any analysis where the main goal is to estimate Y or predict future values. (includes all ML techniques)