ArticleEuropean journal of epidemiology2017
For and Against Methodologies: Some Perspectives on Recent Causal and Statistical Inference Debates.
Article in European journal of epidemiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed.
- Article
- Negatives about positivity and consistency as conditions for causal inference.American journal of epidemiology · 2026Article
- Evaluating treatment benefit predictors using observational data: contending with identification and confounding bias.American journal of epidemiology · 2026Article
- Behavioural and metabolic mediators of socioeconomic inequalities in type 2 diabetes: comparing counterfactual and traditional mediation analysis.European journal of public health · 2025Article
- Strengthening health technology assessment for cancer treatments in Europe by integrating causal inference and target trial emulation.The Lancet regional health. Europe · 2025Review
- Epidemiological Principles in Claims of Causality: An Enquiry into Repetitive Head Impacts (RHI) and Chronic Traumatic Encephalopathy (CTE).Sports medicine (Auckland, N.Z.) · 2025Review
- Considerations and targeted approaches to identifying bad actors in exposure mixtures.Statistics in biosciences · 2024Article
- Interpreting and coding causal relationships for quality and safety using ICD-11.BMC medical informatics and decision making · 2023Review
- Interpreting Randomized Controlled Trials.Cancers · 2023Review
- Neighbours and relatives: accounting for spatial distribution when testing causal hypotheses in cultural evolution.Evolutionary human sciences · 2023Article
- Biases arising from linked administrative data for epidemiological research: a conceptual framework from registration to analyses.European journal of epidemiology · 2022Article
- Recommendations for Using Causal Diagrams to Study Racial Health Disparities.American journal of epidemiology · 2022Article
- Conducting Real-world Evidence Studies on the Clinical Outcomes of Diabetes Treatments.Endocrine reviews · 2021Article
- Handling Uncertainty in Models of Seismic and Postseismic Hazards: Toward Robust Methods and Resilient Societies.Risk analysis : an official publication of the Society for Risk Analysis · 2021Article
- Qualitative Approximations to Causality: Non-Randomizable Factors in Clinical Psychology.Clinical psychology in Europe · 2021Article
- Estimating the Effect of Healthcare-Associated Infections on Excess Length of Hospital Stay Using Inverse Probability-Weighted Survival Curves.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2020Article
- INFERENCE: An Evidence-Based Approach for Medicolegal Causal Analyses.International journal of environmental research and public health · 2020Article
- Understanding Marginal Structural Models for Time-Varying Exposures: Pitfalls and Tips.Journal of epidemiology · 2020Article
- Improving the accuracy of medical diagnosis with causal machine learning.Nature communications · 2020Article
- The Epidemiologic Toolbox: Identifying, Honing, and Using the Right Tools for the Job.American journal of epidemiology · 2020Article
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Abstract
I present an overview of two methods controversies that are central to analysis and inference: That surrounding causal modeling as reflected in the "causal inference" movement, and that surrounding null bias in statistical methods as applied to causal questions. Human factors have expanded what might otherwise have been narrow technical discussions into broad philosophical debates. There seem to be misconceptions about the requirements and capabilities of formal methods, especially in notions that certain assumptions or models (such as potential-outcome models) are necessary or sufficient for valid inference. I argue that, once these misconceptions are removed, most elements of the opposing views can be reconciled. The chief problem of causal inference then becomes one of how to teach sound use of formal methods (such as causal modeling, statistical inference, and sensitivity analysis), and how to apply them without generating the overconfidence and misinterpretations that have ruined so many statistical practices.
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