107 Can synthetic controls improve causal inference in interrupted time series evaluations of public health interventions?
Bibliographic record
Abstract
Statement of Purpose Interrupted time series (ITS) designs are a valuable quasi-experimental approach for evaluating public health interventions. ITS extends a single group pre-post comparison by using multiple timepoints to control for underlying trends. But history bias – confounding by unexpected events occurring at the same time of the intervention – threatens the validity of this design and limits causal inference. Synthetic control methodology (SCM), a popular data-driven technique for deriving a control series from a pool of unexposed populations, is increasingly recommended. We aimed to evaluate if and when SCM can strengthen an ITS design. Methods/Approach First, we summarise the main observational study designs used in evaluative research, highlighting their respective uses, strengths, biases, and design extensions. Second, we outline when the use of SCM can strengthen ITS studies and when their combined use may be problematic. Third, we provide recommendations for using SCM in ITS and, using a real-world example of an evaluation of Florida’s Stand Your Ground laws on homicides, we illustrate the potential pitfalls of using a data-driven approach to identify a suitable control series. Results Our real-world evaluation demonstrates that the benefits of SCM in ITS depends on the nature of the time-varying confounding which presents the most plausible threat to the study’s validity. We emphasise the importance of theoretical approaches for informing study design and argue that synthetic control methods are not always well-suited for minimising critical threats to ITS studies. Conclusions Advances in SCM bring new opportunities to conduct rigorous research in evaluating public health interventions. However, incorporating synthetic controls in ITS studies may not always nullify important threats to validity nor improve causal inference. Significance and Contributions to Injury and Violence Prevention Science We provide important methodological recommendations to guide advancement in the science of injury and violence prevention.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.448 | 0.749 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".