#statstab #506 The VIF Score. What is it Good For? Absolutely Nothing
Thoughts: Maybe we need to think more closely about what these metrics mean.
#diagnostics #assumptions #regression #vif #variance #variableselection #error #multicolinearity
https://journals.sagepub.com/doi/abs/10.1177/10944281231216381
#statstab #380 Variable Importance Plots {vip}
Thoughts: Not sure how useful these are for researchers, but it's one way to plot the "importance" of your model.
#MachineLearning #lasso #ML #regression #vip #variableselection #r
Essay on "Principles of confounder selection":
https://link.springer.com/article/10.1007/s10654-019-00494-6
"[...] the following proposal is made for covariate control decisions: control for each covariate that is a cause of the exposure, or of the outcome, or of both; exclude from this set any variable known to be an instrumental variable; and include as a covariate any proxy for an unmeasured variable that is a common cause of both the exposure and the outcome."
Selecting an appropriate set of confounders for which to control is critical for reliable causal inference. Recent theoretical and methodological developments have helped clarify a number of principles of confounder selection. When complete knowledge of a causal diagram relating all covariates to each other is available, graphical rules can be used to make decisions about covariate control. Unfortunately, such complete knowledge is often unavailable. This paper puts forward a practical approach to confounder selection decisions when the somewhat less stringent assumption is made that knowledge is available for each covariate whether it is a cause of the exposure, and whether it is a cause of the outcome. Based on recent theoretically justified developments in the causal inference literature, the following proposal is made for covariate control decisions: control for each covariate that is a cause of the exposure, or of the outcome, or of both; exclude from this set any variable known to be an instrumental variable; and include as a covariate any proxy for an unmeasured variable that is a common cause of both the exposure and the outcome. Various principles of confounder selection are then further related to statistical covariate selection methods.
Dr Mircea Zloteanu 🌺🌞🍃
Jan R. Boehnke