Using results to inform decisions

Understanding your PA results is only valuable if it leads to better decisions. This page bridges the gap between analysis and action — drawing on the results you have explored in the previous pages to think about how to put them to use.

Allocating resources across sites

Distribution plots like the ones shown in the previous pages are especially useful for resource allocation decisions. When you can see which sites have the highest concentrations of high-risk individuals — and how large those sites are — you have a concrete basis for prioritizing where to direct limited resources.

For example, if one school has a large share of students with predicted dropout probabilities above 0.75, while another has very few, a program director might reasonably target additional support to the first school first. The key is that this decision is now grounded in systematic evidence rather than intuition alone.

When making these recommendations, be transparent about the threshold values driving the categorization. The choice of where to draw the line between “high risk” and “moderate risk” directly affects which sites and individuals get prioritized. This is worth surfacing explicitly in stakeholder conversations.

Thinking about intervention design

Variable importance results and group-level analyses can inform thinking about what kinds of interventions might be most effective. If absenteeism is the strongest predictor of dropout risk and is highly prevalent among high-risk students, this may point toward interventions focused on attendance — rather than, say, academic tutoring alone.

However, this reasoning requires care. As emphasized earlier, VI and group-level associations reflect correlations in your data, not causal relationships. The fact that absenteeism is associated with dropout risk does not mean that reducing absenteeism will reduce dropout rates — other factors may be driving both. Use these findings to generate hypotheses and focus program attention, not to draw firm conclusions about what will work.

Ideally, PA results are interpreted alongside the contextual knowledge of practitioners who understand the population. They may recognize patterns in the data that make sense given their experience — or flag findings that seem off and warrant further investigation.

Connecting predictions to decisions about individuals

In some public services contexts, PA results are used not just for population-level resource allocation but to inform decisions about specific individuals — for example, which students to reach out to, or which families to prioritize for case manager contact.

When results are used this way, a few additional considerations apply:

• Predicted probabilities are not decisions. A high predicted probability flags someone as potentially needing attention — it does not determine what should happen next. Professional judgment remains essential.
• Consider the full range of risk. High-risk individuals are the most obvious targets for intervention, but those in the moderate-risk range may also benefit significantly, and in some cases may be more responsive to intervention than those at the highest risk.
• Be thoughtful about how predictions are communicated to frontline staff. Caseworkers and program staff will interact with the people these predictions describe. How predictions are framed — and what staff are told to do with them — matters for both effectiveness and equity.