This paper will describe the logic and performance (in simulations) of the CAMIC method, using the Health Profession Opportunity Grants (HPOG) evaluation as an example of its potential to expand policy learning when evaluators and policymakers seek ways to improve—not just assess, up or down—studied social programs. Examining impact variation across sites in an attempt to learn which program components lead to larger impacts is something of obvious policy importance. But as is well known, nonexperimental attribution of site-to-site impact differences to potential causal factors—both program features and contextual and participant characteristics—can give misleading guidance to program designers wishing to maximize the impact of future evidence-based interventions. If the evaluator omits from the analytic model determinants of impact that correlate with included program features or makes mistaken functional form assumptions, then the wrong conclusions will be reached—and the error likely will never be detected. CAMIC reduces the initial risk, and provides the opportunity to test for any remaining error. Reliance on cross-site modeling of impacts for policy guidance can thus be strengthened and assessed all in one study. (Excerpt from author introdcution)
How three-arm random assignment within sites can improve non-experimental cross-site estimates of the relationship between program characteristics and impact
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