I'm a little more bothered by the glibness of moving from 'we have a good prediction/regression algorithm' to 'we can causally control and optimize connections

I'm a little more bothered by the glibness of moving from 'we have a good prediction/regression algorithm' to 'we can causally control and optimize connections using it'. When they claim in the abstract that > Based on the measurement analysis, we develop a machine learning based AP selection strategy that can significantly improve WiFi connection set-up performance, against the conventional strategy purely based on signal strength, by reducing the connection set-up failures from 33% to 3.6% and reducing 80% time costs of the connection set-up processes by more than 10 times. They don't actually demonstrate this and don't know that it does improve WiFi connection performance, because they don't benchmark it on any real-world devices. It's pure extrapolation from the algorithm's predictive performance on the dataset. (And there are some suspicious inputs to the algorithm like time of day.) When they write on pg9 about where they are pulling these numbers from: > To evaluate, we first divide our connection log dataset into two parts, each subset contains 50% of the overall data. ...This fresh dataset ensures that we can accurately evaluate the performance of our algorithm if we deploy our algorithm in the wild, where many of the APs will not be seen by the mobile devices before. They're just wrong. Splitting like this only ensures good out-of-sample performance when drawing from the same distribution, but when you use the algorithm to make choices, the distribution is different. Correlation!=causation; it's no more guaranteed to help than data mining hospital records and finding that antibiotics apparently are killing patients and so hospitals should stop using them.