  In this paper, we propose Forest-PLS, a feature selection method for
analyzing policy effect heterogeneity in a more flexible and comprehensive
manner than is typically available with conventional methods. In particular,
our method is able to capture policy effect heterogeneity both within and
across subgroups of the population defined by observable characteristics. To
achieve this, we employ partial least squares to identify target components of
the population and causal forests to estimate personalized policy effects
across these components. We show that the method is consistent and leads to
asymptotically normally distributed policy effects. To demonstrate the efficacy
of our approach, we apply it to the data from the Pennsylvania Reemployment
Bonus Experiments, which were conducted in 1988-1989. The analysis reveals that
financial incentives can motivate some young non-white individuals to enter the
labor market. However, these incentives may also provide a temporary financial
cushion for others, dissuading them from actively seeking employment. Our
findings highlight the need for targeted, personalized measures for young
non-white male participants.
