Towards robust causal inference in epidemiologic research: employing double cross-fit TMLE in right heart catheterization data
Bibliographic record
Abstract
Within epidemiologic research, estimating treatment effects from observational data presents notable challenges. Targeted maximum likelihood estimation (TMLE) emerges as a robust method, addressing these challenges by accurately modeling treatment effects. This approach uniquely combines the precision of correctly specified models with the versatility of data-adaptive, flexible machine learning algorithms. Despite its effectiveness, TMLE's integration of complex algorithms can introduce bias and undercoverage. This issue is addressed through the double cross-fit TMLE (DC-TMLE) approach, enhancing accuracy and reducing biases inherent in observational studies. However, DC-TMLE's potential remains underexplored in epidemiologic research, primarily due to the lack of comprehensive methodologic guidance and the complexity of its computational implementation. Recognizing this gap, our article contributes a detailed, reproducible guide for implementing DC-TMLE in R, aimed specifically at epidemiologic applications. We demonstrate the utility of this method using an openly available clinical data set, underscoring its relevance and adaptability for robust epidemiologic analysis. This guide aims to facilitate broader adoption of DC-TMLE in epidemiologic studies, promoting more accurate and reliable treatment effect estimations in observational research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.065 | 0.249 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".