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

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TLDR
Natural experiment (NE) approaches are attracting growing interest as a way of providing evidence in such circumstances as discussed by the authors, and one key challenge in evaluating NEs is selective exposure to the intervention.
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 ...

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Journal ArticleDOI

Designing Difference in Difference Studies: Best Practices for Public Health Policy Research

TL;DR: Key features of DID designs are reviewed with an emphasis on public health policy research and it is noted that combining elements from multiple quasi-experimental techniques may be important in the next wave of innovations to the DID approach.
Proceedings Article

What is Causal Inference

Abstract: This paper reviews a theory of causal inference based on the Structural Causal Model (SCM) described in (Pearl, 2000a). The theory unifies the graphical, potential-outcome (Neyman-Rubin), decision analytical, and structural equation approaches to causation, and provides both a mathematical foundation and a friendly calculus for the analysis of causes and counterfactuals. In particular, the paper establishes a methodology for inferring (from a combination of data and assumptions) the answers to three types of causal queries: (1) queries about the effect of potential interventions, (2) queries about counterfactuals, and (3) queries about the direct (or indirect) effect of one event on another.
Journal ArticleDOI

Climate Change and Global Food Systems: Potential Impacts on Food Security and Undernutrition.

TL;DR: The main pathways by which climate change may affect the authors' food production systems-agriculture, fisheries, and livestock-as well as the socioeconomic forces that may influence equitable distribution are reviewed.
References
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Journal ArticleDOI

The Strengthening the Reporting of Observational Studies in Epidemiology [STROBE] statement: guidelines for reporting observational studies

TL;DR: The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) initiative developed recommendations on what should be included in an accurate and complete report of an observational study, resulting in a checklist of 22 items (the STROBE statement) that relate to the title, abstract, introduction, methods, results, and discussion sections of articles.
Book

Experimental and Quasi-Experimental Designs for Generalized Causal Inference

TL;DR: In this article, the authors present experiments and generalized Causal inference methods for single and multiple studies, using both control groups and pretest observations on the outcome of the experiment, and a critical assessment of their assumptions.
Journal ArticleDOI

An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies

TL;DR: The propensity score is a balancing score: conditional on the propensity score, the distribution of observed baseline covariates will be similar between treated and untreated subjects, and different causal average treatment effects and their relationship with propensity score analyses are described.
Journal ArticleDOI

Closing the gap in a generation: health equity through action on the social determinants of health

TL;DR: The Commission on Social Determinants of Health (CSDH) as mentioned in this paper was created to marshal the evidence on what can be done to promote health equity and to foster a global movement to achieve it.
Journal ArticleDOI

Propensity score methods for bias reduction in the comparison of a treatment to a non‐randomized control group

TL;DR: The propensity score, defined as the conditional probability of being treated given the covariates, can be used to balance the variance of covariates in the two groups, and therefore reduce bias as mentioned in this paper.
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