Worse, if there are variables selection issue also for description purpose this selection looking for what? It seems me that in this case conflations, mainly toward causation side, hardly will go away. Why? It seems me that no dependencies is a respectable descriptive result. Because she say that significance (difference from 0) are relevant for the variable to “remain” in the model. However, unfortunately yet, at slide 23 Shmueli says something that contradict my position. This my related question can help ( Joint probability distributions and regression) This can be a reply for my question above. In my mind I justify, but not appreciate much, the existence of description so: given the variables of interest I estimate regressions in order to reveal pure statistical dependencies.

Infact, let me note that a couple of slides was insert for underscore the core difference between: prediction-causation and prediction-description but no one for causation-description. However it seem me that dangerous conflating area remain between causation and description. I understand that description is markedly different from prediction because, first of all, overfitting and related out of sample measures are the core for prediction while become irrelevant for description scope. However, unfortunately, too few words are spent about description yet. These slide speak precisely about the confusion that again exist in both Academia and Industry about the role of regression.

To Explain To Predict or To Describe? – satellite conference August 15-16 2019 Kuala Lumpur (Malaysia). I have found in the web a set of recent slide where Shmueli underscore more clearly that the role of description exist and that is different from others two. My main question is precisely: what description meaning is? What are the main distinction from description and the other two role for regression? Can you give me a simple as possible example where those differences emerge clearly?īefore to reply, to read the follow seem me needed. Unfortunately those word are very few and the only clear thing that we can understand from those are: description is different from both causation and prediction the role of description are much less important than the others two. However in his article Shmueli spent some word about description.

I asked and replied in some related post in this site, as example:

To now I’m feel myself more confident about the distinction between prediction and causation. In last article the Author use the word explanation as synonym of causation. Great light was done to me by this article: Shmueli - To explain or to predict - Statistical Science 2010, Vol. My perplexity became both, confirmed and go deeper, when I read: Regression and Causation: A Critical Examination of Six Econometrics Textbooks - Chen and Pearl (2013). I started to study regression by Econometrics side and I started to be puzzled, some years ago, thinking about regression and its causal role. It seems me that confusions are not go away yet. In my experience it seems me that the interpretation about regression, its meaning and its scope, are debatable and great confusion exist about those things.

Causality vs causation