I know about the benefits of regularization when building predictive models (bias vs. variance, preventing overfitting). But, I'm wondering if it is a good idea to also do regularization (lasso, ridge, elastic net) when the main purpose of the regression model is inference on the coefficients (seeing which predictors are statisically significant). I'd love to hear people's thoughts as well as links to any academic journals or non-academic articles addressing this.

I know about the benefits of regularization when building predictive models (bias vs. variance, preventing overfitting). But, I'm wondering if it is a good idea to also do regularization (lasso, ridge, elastic net) when the main purpose of the regression model is inference on the coefficients (seeing which predictors are statistically significant). I'd love to hear people's thoughts as well as links to any academic journals or non-academic articles addressing this.