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Review
. 2017 Mar 20:38:39-56.
doi: 10.1146/annurev-publhealth-031816-044327. Epub 2017 Jan 11.

Natural Experiments: An Overview of Methods, Approaches, and Contributions to Public Health Intervention Research

Peter Craig  1 , Srinivasa Vittal Katikireddi  1 , Alastair Leyland  1 , Frank Popham  1
Affiliations
Review

Natural Experiments: An Overview of Methods, Approaches, and Contributions to Public Health Intervention Research

Peter Craig et al. Annu Rev Public Health. .

Abstract

Population health interventions are essential to reduce health inequalities and tackle other public health priorities, but they are not always amenable to experimental manipulation. Natural experiment (NE) approaches are attracting growing interest as a way of providing evidence in such circumstances. One key challenge in evaluating NEs is selective exposure to the intervention. Studies should be based on a clear theoretical understanding of the processes that determine exposure. Even if the observed effects are large and rapidly follow implementation, confidence in attributing these effects to the intervention can be improved by carefully considering alternative explanations. Causal inference can be strengthened by including additional design features alongside the principal method of effect estimation. NE studies often rely on existing (including routinely collected) data. Investment in such data sources and the infrastructure for linking exposure and outcome data is essential if the potential for such studies to inform decision making is to be realized.

Keywords: causal inference; evaluation methods; population health interventions.

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Figures

Figure 1
Figure 1. Directed acyclic graphs illustrating the assumptions of instrumental variable (IV) analysis.
(a) The variable Z is associated with outcome Y only through its association with exposure X, so it can be considered a valid instrument of X. (b) Z is not a valid instrument owing to a lack of any association with outcome Y. (c) Z is not a valid instrument owing to its association with confounder C. (d) Z is not a valid instrument owing to its direct association with Y.
Figure 2
Figure 2. Probability of receiving treatment in fuzzy and sharp regression discontinuity designs.
(a) A fuzzy regression discontinuity: probability of treatment changes gradually at values of the assignment variable close to the cutoff. (b) A sharp regression discontinuity: probability of treatment changes from 0 to 1 at the cutoff. Source: Reproduced from Moscoe (2015) (57) with permission from Elsevier.
See this image and copyright information in PMC

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