Why your causal diagram should probably include sampling and measurement processes

2026 · doi:10.32942/x28q1q
preprint OA: closed
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The paper argues that causal diagrams used in insect science should explicitly include sampling and measurement processes, because observed associations depend on those observation mechanisms. Drawing on cross-disciplinary literature, it explains how to incorporate assumptions about how data are collected and measured into causal diagrams and structural causal reasoning, not only for causal interpretation of associations but also for descriptive and predictive goals. A major limitation acknowledged by the text is that it is a preprint and not peer reviewed, and it provides a conceptual framework rather than results from a specific empirical study. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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Abstract

Insect scientists are starting to use causal diagrams to display assumptions about causal relationships between variables that exist before any data have been collected. The perception appears to be that these assumptions are sufficient to determine whether observed associations between variables can be interpreted causally. But an observed association implies an observation process (sampling and measurement), and assumptions about that process are also required. We draw on the literature from other disciplines to explain how insect scientists can incorporate assumptions about sampling and measurement in causal diagrams. Making these assumptions explicit allows the investigator to reason more holistically about whether observed associations can be interpreted as causal effects. It also reveals that causal diagrams are not just a tool for causal inference. Assumptions about sampling and measurement are needed to answer descriptive and predictive questions as well. Hence, causal diagrams that incorporate these processes provide a general framework for displaying assumptions regardless of inferential goal.
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This is a Preprint and has not been peer reviewed. This is version 1 of this Preprint. You must log in to post a comment. There are no comments or no comments have been made public for this article. This is a Preprint and has not been peer reviewed. This is version 1 of this Preprint. Add a Comment You must log in to post a comment. Comments There are no comments or no comments have been made public for this article. Insect scientists are starting to use causal diagrams to display assumptions about causal relationships between variables that exist before any data have been collected. The perception appears to be that these assumptions are sufficient to determine whether observed associations between variables can be interpreted causally. But an observed association implies an observation process (sampling and measurement), and assumptions about that process are also required. We draw on the literature from other disciplines to explain how insect scientists can incorporate assumptions about sampling and measurement in causal diagrams. Making these assumptions explicit allows the investigator to reason more holistically about whether observed associations can be interpreted as causal effects. It also reveals that causal diagrams are not just a tool for causal inference. Assumptions about sampling and measurement are needed to answer descriptive and predictive questions as well. Hence, causal diagrams that incorporate these processes provide a general framework for displaying assumptions regardless of inferential goal. https://doi.org/10.32942/X28Q1Q Life Sciences Directed Acyclic graph, structural causal model, insect declines Published: 2026-03-11 16:20 Last Updated: 2026-03-11 16:20 CC-BY Attribution-NonCommercial 4.0 International Conflict of interest statement: None to disclose. Data and Code Availability Statement: NA Language: English

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