A classic example in tech is estimating the effect of a new feature that was released to all the user base at once: no A/B test was done and there’s absolutely no one that could be the control group. In this case, you can try making a counterfactual estimation.
The idea behind counterfactual estimation is to create a model that allows you to compute a counterfactual control group. In other words, you estimate what would happen had this feature not existed. It isn’t always simple to compute an estimate. However, if you have a model of your users that you’re confident about, then you have enough material to start doing counterfactual causal analyses!
Below is an example of time series counterfactual vs. observed data by Shopify