Combining the power of experiments with the potential of machine learning has tremendous implications for designing more effective public policy.
... Ultimately, the researchers concluded that the most effective policy is to target nudges to the middle of the group — students who are neither the most nor least likely to reapply for financial aid. At either end of this spectrum, the power of the nudge weakens, particularly for those who are the least likely to apply for aid. ...
The machine learning model, in essence, can help mitigate concerns of external validity by fitting the results from one population to another distinct population.
This hybrid approach has the potential to make experimentation less expensive by supporting faster iteration. As an experiment is running, machine learning can discern what works and suggest ways to fine-tune interventions in real time for maximum impact.
For policymakers, this adaptable, targeted process provides the ability to move beyond catchall approaches that are often costly and marginally effective. ...
The method, Spiess says, also illuminates blind spots — such as the people who are left behind by certain interventions.
See the full story here; https://www.gsb.stanford.edu/insights/ai-can-help-personalize-policies-reach-right-people