I have trained an XGBoost Classifier and I am now trying to explain how and, most importantly, why the model has made the predictions it's made. In the documentation entry Be careful when interpreting predictive models in search of causal insights, I have read that SHAP values are indicative of correlation but not causation. More specifically: SHAP makes transparent the correlations picked up by predictive ML models. But making correlations transparent does not make them causal! All predictive models implicitly …

We have a data table that accumulates the control and monitoring parameters of the High-Temperature Superconductor (HTS) production process: such that the rows represent the observations and columns represent the parameters mentioned above. Due to the nature of the production process, there are time dependencies between the rows of our data sets. Thus the columns, are, indeed, time series. (Which boils down our data to time-dependent data.) Now the question arises: whether we can apply induced causation methods, explained in …

Say I have a bike sharing company and want to know how events affect number of rides per day. events vary on: number of coupons sent per user expiry date amount for each coupon. I have created a df that looks something like this: deploy duration avg_temp rain register_count ... rent_count 33 131113 23 2.2 12 33 523 931143 25 0 322 756 63 231153 26 0 111 124 123 566363 22 1.2 334 345 where rent_count is our target …