ArticleEvolutionary human sciences2024
Methods in causal inference. Part 2: Interaction, mediation, and time-varying treatments.
Article in Evolutionary human sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis 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.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Correlation does not equal causation: the imperative of causal inference in machine learning models for immunotherapy.Frontiers in immunology · 2025Pooled it
- Causal Effect of Psychopathy on Partner Well-Being: A National Longitudinal Study.Journal of personality · 2026Article
- Investigating the causal effects of religiosity on childbearing among U.S. adolescents using a three-wave longitudinal design.Scientific reports · 2026Article
- The longitudinal associations of material security and belief in God in young Americans.Evolutionary human sciences · 2026Article
- Target trial emulation shows that supported causal effects of religious attendance on well-being are selective.Evolutionary human sciences · 2026Article
- 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
- Integrating Exposome into Lifecourse Understanding of Cognitive Ageing and Dementia: Current Evidence, Methodological Challenges, and Future Directions.International journal of environmental research and public health · 2025Article
- Methods in causal inference. Part 4: confounding in experiments.Evolutionary human sciences · 2024Article
- Methods in causal inference. Part 1: causal diagrams and confounding.Evolutionary human sciences · 2024Article
- Methods in causal inference. Part 3: measurement error and external validity threats.Evolutionary human sciences · 2024Article
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The analysis of 'moderation', 'interaction', 'mediation' and 'longitudinal growth' is widespread in the human sciences, yet subject to confusion. To clarify these concepts, it is essential to state causal estimands, which requires the specification of counterfactual contrasts for a target population on an appropriate scale. Once causal estimands are defined, we must consider their identification. I employ causal directed acyclic graphs and single world intervention graphs to elucidate identification workflows. I show that when multiple treatments exist, common methods for statistical inference, such as multi-level regressions and statistical structural equation models, cannot typically recover the causal quantities we seek. By properly framing and addressing causal questions of interaction, mediation, and time-varying treatments, we can expose the limitations of popular methods and guide researchers to a clearer understanding of the causal questions that animate our interests.
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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 Socratic graph.