Navigating Unmeasured Confounding in Nonexperimental Psychological Research: A Practical Guide to Computing and Interpreting E-Value
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Randomized experiments remain the “gold standard” for establishing causality, yet ethical and practical constraints in certain fields often require researchers to rely on observational data. Although psychologists recognize that correlation does not imply causality, the conventional cautionary statements regarding correlation typically found at the end of articles have not sufficiently advanced psychological science, particularly in subfields, such as developmental and personality psychology, that predominantly rely on observational data. Sensitivity analyses commonly used in biostatistics and epidemiology offer powerful tools to quantify the risk of unmeasured confounding in observational data analysis, essentially encouraging applied researchers to assess how strongly an unmeasured confounder must be associated with both the predictor and outcome to negate an observed predictor-outcome association (i.e., reduce the effect to null). In this tutorial, we explore the frequently overlooked but critical issue of unmeasured confounding in psychological research and introduce psychologists to the E-value, a novel and straightforward method for assessing the robustness of exposure-outcome associations to unmeasured confounding. We demonstrate the application of E-value using common psychological-research scenarios in R and discuss its strengths, limitations, and recommended best practices. Psychologists can more accurately assess and transparently report research findings, particularly in subfields relying primarily on observational data, by more explicitly considering unmeasured confounding and incorporating sensitivity-analysis techniques such as the E-value into their methodological tool kits.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| grok | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | high |
| opus | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | medium |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.036 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it