Can synthetic controls improve causal inference in interrupted time series evaluations of public health interventions?

Degli Esposti, M., Spreckelsen, T. F. , Gasparrini, A., Wiebe, D. J., Bonander, C., Yakubovich, A. R. and Humphreys, D. K. (2020) Can synthetic controls improve causal inference in interrupted time series evaluations of public health interventions? International Journal of Epidemiology, 49(9), pp. 2010-2020. (doi: 10.1093/ije/dyaa152) (PMID:33005920)

Abstract

Interrupted time series designs are a valuable quasi-experimental approach for evaluating public health interventions. Interrupted time series extends a single group pre-post comparison by using multiple time points 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, a popular data-driven technique for deriving a control series from a pool of unexposed populations, is increasingly recommended. In this paper, we evaluate if and when synthetic controls can strengthen an interrupted time series design. First, we summarize the main observational study designs used in evaluative research, highlighting their respective uses, strengths, biases and design extensions for addressing these biases. Second, we outline when the use of synthetic controls can strengthen interrupted time series studies and when their combined use may be problematic. Third, we provide recommendations for using synthetic controls in interrupted time series and, using a real-world example, we illustrate the potential pitfalls of using a data-driven approach to identify a suitable control series. Finally, we emphasize the importance of theoretical approaches for informing study design and argue that synthetic control methods are not always well suited for generating a counterfactual that minimizes critical threats to interrupted time series studies. Advances in synthetic control methods bring new opportunities to conduct rigorous research in evaluating public health interventions. However, incorporating synthetic controls in interrupted time series studies may not always nullify important threats to validity nor improve causal inference.

• Item Type: Articles
• Keywords: Interrupted time series, synthetic controls, quasi-experimental, causal inference, history bias.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Spreckelsen, Dr Thees
• Authors: Degli Esposti, M., Spreckelsen, T. F., Gasparrini, A., Wiebe, D. J., Bonander, C., Yakubovich, A. R., and Humphreys, D. K.
• Subjects: H Social Sciences > H Social Sciences (General); H Social Sciences > HA Statistics; R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine
• College/School: College of Social Sciences > School of Social and Political Sciences > Sociology Anthropology and Applied Social Sciences
• Journal Name: International Journal of Epidemiology
• Journal Abbr.: Int J Epidemiol
• Publisher: Oxford University Press
• ISSN: 0300-5771
• ISSN (Online): 1464-3685
• Published Online: 02 October 2020
• Copyright Holders: Copyright © The Author(s) 2020
• First Published: First published in International Journal of Epidemiology 49(9):2010-2020
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

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