For outliers treatment, clipping, winsorizing or removing?
On the difference between winsorizing and clipping : 


The techniques are very similar. They deal with extreme values (that are not necessarily outliers). Imo you should generally avoid thinking that big values = outliers. Solutions to deal with big values include normalizing your variables by a size factor for more comparability. 
Which one should you use is linked to your data and their predictiveness. Sometimes data outside of a threshold will be predictive, sometimes not. Sometimes the underlying process will depend on rank, sometimes on global values. There is no rule for when to do one of those two techniques, there is no rule on which one to choose. In the end, you should choose which one gives you better results. Yeah, windsorisation seems a bit more 'adaptative' as you don't have to look at each of your predictors to choose a threshold, but outside of that, there is no reason it performs better statistically. 
One of the main problems of those techniques is that they are univariate. If your predictors are correlated, modifying one value without another can breaks your relationships.
Yes, the answer is model dependent. Having extreme value may be detrimental for the convergence of methods based on gradient descent, as some data points may swing the optimization path. For methods relying on the partition of the space like trees, having extreme values is less of a problem.


Removing outliers is a completely different strategy. The difficulty is to identify outliers confidently... but that's another question.