Given the complex relationships between patients’ demographics, underlying health needs, and outcomes, establishing the causal effects of health policy and delivery interventions on health outcomes is often empirically challenging. The single interrupted time series (SITS) design has become a popular evaluation method in contexts where a randomized controlled trial is not feasible. In this paper, we formalize the structure and assumptions underlying the single ITS design and show that it is significantly more vulnerable to confounding than is often acknowledged and, as a result, can produce misleading results. We illustrate this empirically using the Oregon Health Insurance Experiment, showing that an evaluation using a single interrupted time series design instead of the randomized controlled trial would have produced large and statistically significant results of the wrong sign. We discuss the pitfalls of the SITS design, and suggest circumstances in which it is and is not likely to be reliable.

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