Ask any programmer what their favourite programming book is, and their answer will be one of the usual suspects: Code Complete, The Pragmatic Programmer, or Design Patterns. And rightly so; these are outstanding and highly-regarded works that belong to every programmer’s bookshelf. (If you’re just starting out building up your bookshelf, Jeff Atwood has some […]
Many statistical learning algorithms perform better when the covariates are on similar scales. For example, it is common practice to standardize the features used by an artificial neural network so that the gradient of its objective function doesn’t depend on the physical units in which the features are described. The same advice is frequently given […]
When observational data includes a treatment indicator and some possible confounders, it is very tempting to simply regress the outcome on all features (confounders and treatment alike), extract the coefficients associated with the treatment indicator, and proudly proclaim that “we have controlled for confounders and estimated the treatment effect”. This approach is wrong. Very wrong. […]