Exam DP-100 topic 5 question 37 discussion

I would say explanation rather than causal. Why not explanation?

correct, BD

Use what-if counterfactuals when you need to: Examine fairness and reliability criteria as a decision evaluator by perturbing sensitive attributes such as gender and ethnicity, and then observing whether model predictions change. Debug specific input instances in depth. Provide solutions to users and determine what they can do to get a desirable outcome from the model. https://learn.microsoft.com/en-us/azure/machine-learning/concept-counterfactual-analysis?view=azureml-api-2 Finally, if we wanted to purely use historic data to identify the features that have the most direct effect on our outcome of interest, in this case the score, we can use causal analysis.