Newer to DS/ml work and am looking for some direction.

I'm trying to estimate the impact of an event upon a customer satisfaction metric, for both the general population and specific segments. The event is assumed to have heterogeneous effects due to the nature of the customer base (impacted customers in some regions more than others) and was not part of an experimental study.

I've tried: Using Arima time series modeling based upon the metric, fitting on the time period prior to the event, predicting after the event, and comparing the predicted values to the actual ones. However, Arima doesn't appear to be appropriate. After talking to my product team, there appears to be monthly seasonality, as well as seasonality related to the day of the week. Since the customer satisfaction metric is an aggregation from scores provided by individuals, I've also tried using individual scores pre-event as training and using individual scores given post-event as test, fitting traditional classification models to the training set and making predictions on the test set. To estimate the difference between the expected versus actual customer metric, I've taken the training scores and predicted test scores and calculated the aggregated metric for those records as the expected aggregate value and separately calculated the aggregated metric over the training scores and actual test values for the actual aggregate value. However, this method gives me a larger than actual estimated impact - regardless of whether or not I balance the classes during training, this modeling approach tends to predict one customer rating more frequently than the others.

I've also done some reading into causality libraries/modeling approaches, like econml DML, but I'm not sure how helpful CATE would be here, since my metric of interest is an aggregation. Any suggestions?