(Machine) Learning What Policies Value
This provides a tool for auditing and designing policies to better align with values, addressing a problem for policymakers and social scientists, though it is incremental as it applies existing machine learning methods to a new context.
The paper tackles the problem of inferring the underlying values behind policy allocation decisions by developing a method to estimate individual benefits and reconcile them with welfare weights, heterogeneous treatment effects, and outcome weights, as demonstrated in Mexico's PROGRESA program, revealing that prioritized subgroups like indigenous households had lower welfare weights despite benefiting more.
When a policy prioritizes one person over another, is it because they benefit more, or because they are preferred? This paper develops a method to uncover the values consistent with observed allocation decisions. We use machine learning methods to estimate how much each individual benefits from an intervention, and then reconcile its allocation with (i) the welfare weights assigned to different people; (ii) heterogeneous treatment effects of the intervention; and (iii) weights on different outcomes. We demonstrate this approach by analyzing Mexico's PROGRESA anti-poverty program. The analysis reveals that while the program prioritized certain subgroups -- such as indigenous households -- the fact that those groups benefited more implies that they were in fact assigned a lower welfare weight. The PROGRESA case illustrates how the method makes it possible to audit existing policies, and to design future policies that better align with values.