I am sharing this MDRC brief that summarizes findings and recommendations from a study designed to measure and compare the added value of models used to predict participant success within career pathways programs in the Health Profession Opportunity Grants (HPOG) Program. HPOG provided education and training in high-demand occupations in the health care field to Temporary Assistance for Needy Families participants and other low-income individuals.
Key findings from the brief include:
- Program outcomes are predictable even when simple, cost-effective data science methods are used.
- Within HPOG 1.0 programs, the most important factor in predicting participant success is prior education level.
- When one powerful indicator is used to predict program outcomes, results show that the simple model is only marginally less accurate than the best machine learning algorithm.
- Complex methods such as machine learning provide small gains in predicting program outcomes compared to simple methods. These small gains should be balanced with consideration of program staff resources, decreased transparency in machine learning methods, and bias from the algorithm that can reinforce existing discrimination and inequity.