ArticleSSM - population health2020
Alternative causal inference methods in population health research: Evaluating tradeoffs and triangulating evidence.
Article in SSM - population health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 4 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
39 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Correlation does not equal causation: the imperative of causal inference in machine learning models for immunotherapy.Frontiers in immunology · 2025Pooled it
- Application of causal inference methods in individual-participant data meta-analyses in medicine: addressing data handling and reporting gaps with new proposed reporting guidelines.BMC medical research methodology · 2024Pooled it
- Punitive legal responses to prenatal drug use in the United States: A survey of state policies and systematic review of their public health impacts.The International journal on drug policy · 2024Pooled it
- How do income changes impact on mental health and wellbeing for working-age adults? A systematic review and meta-analysis.The Lancet. Public health · 2022Pooled it
- Article
- Long-term Cardiovascular Outcomes Following Bariatric Surgery: Reconciling Seemingly Conflicting Evidence.Epidemiology (Cambridge, Mass.) · 2026Article
- Causal Inference in Health Disparities Research.Annual review of public health · 2026Review
- The AhR-TLR4 axis in non-IgE-mediated Cow's milk allergy: a systematic review with integrated multi-omics corroboration.Frontiers in allergy · 2026Review
- Integrating job design and cognitive-emotional processes using latent network analysis: associations with performance and emotional well-being in navy personnel.BMC psychology · 2025Article
- Toward New Directions in Human Biology: A Roadmap for Anthropological Causal Inference With Observational Data.American journal of human biology : the official journal of the Human Biology Council · 2025Article
- Evidence triangulation in health research.European journal of epidemiology · 2025Article
- Evaluating the contribution of a scaled up community-based overweight prevention programme in the Netherlands to children's health behaviours and BMIz.The international journal of behavioral nutrition and physical activity · 2025Observational
- Effectiveness of total hip arthroplasty versus non-surgery on patient-reported hip function at 3 months: a target trial emulation study of patients with osteoarthritis.Acta orthopaedica · 2025Article
- Causal Effect of Smoking and Cessation on Tooth Loss.Journal of clinical periodontology · 2025Article
- Keep asking: What do I want? What do I have? What do I do? : An approach for combining information to learn about a target population.European journal of epidemiology · 2025Article
- Investigating causal effects of income on health using two-sample Mendelian randomisation.BMC global and public health · 2025Article
- Revascularization in frail patients with acute coronary syndromes: a retrospective longitudinal study.European heart journal · 2025Article
- Methods Used to Evaluate the Health Effects of Social Policies: A Systematic Review.Current epidemiology reports · 2025Article
- Quasi-experimental methods for pharmacoepidemiology: difference-in-differences and synthetic control methods with case studies for vaccine evaluation.American journal of epidemiology · 2024Article
- Estimating the causal effects of income on health: how researchers' definitions of "income" matter.BMC public health · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Population health researchers from different fields often address similar substantive questions but rely on different study designs, reflecting their home disciplines. This is especially true in studies involving causal inference, for which semantic and substantive differences inhibit interdisciplinary dialogue and collaboration. In this paper, we group nonrandomized study designs into two categories: those that use confounder-control (such as regression adjustment or propensity score matching) and those that rely on an instrument (such as instrumental variables, regression discontinuity, or differences-in-differences approaches). Using the Shadish, Cook, and Campbell framework for evaluating threats to validity, we contrast the assumptions, strengths, and limitations of these two approaches and illustrate differences with examples from the literature on education and health. Across disciplines, all methods to test a hypothesized causal relationship involve unverifiable assumptions, and rarely is there clear justification for exclusive reliance on one method. Each method entails trade-offs between statistical power, internal validity, measurement quality, and generalizability. The choice between confounder-control and instrument-based methods should be guided by these tradeoffs and consideration of the most important limitations of previous work in the area. Our goals are to foster common understanding of the methods available for causal inference in population health research and the tradeoffs between them; to encourage researchers to objectively evaluate what can be learned from methods outside one's home discipline; and to facilitate the selection of methods that best answer the investigator's scientific questions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.