A central goal of accountable care organizations (ACOs) is to improve the health of their accountable population. No evidence currently links ACO development to improved population health. A major challenge to establishing the evidence base for the impact of ACOs on population health is the absence of a theoretically grounded, robust, operationally feasible, and meaningful research design. The authors present an evaluation study design, provide an empirical example, and discuss considerations for generating the evidence base for ACO implementation. A quasi-experimental study design using propensity score matching in combination with small-scale exact matching is implemented. Outcome indicators based on claims data were constructed and analyzed. Population health is measured by using a range of mortality indicators: Mortality ratio, age at time of death, years of potential life lost/gained, and survival time. The application is assessed using longitudinal data from Gesundes Kinzigtal, one of the leading population-based ACOs in Germany. The proposed matching approach resulted in a balanced control of observable differences between the intervention (ACO) and control groups. The mortality indicators used indicate positive results. For example, 635.6 fewer years of potential life lost (2005.8 vs. 2641.4; t-test: Sig. P < 0.05∗) in the ACO intervention group (n = 5411) attributable to the ACO, also after controlling for a potential (indirect) immortal time bias by excluding the first half year after enrollment from the outcome measurement. This empirical example of the impact of a German ACO on population health can be extended to the evaluation of ACOs and other integrated delivery models of care.