Implementing TMLE in the presence of a continuous outcome
Bibliographic record
Abstract
In a real-world observational data analysis setting, guessing the true model specification can be difficult for an analyst. Unfortunately, correct model specification is a core assumption for treatment effect estimation methods such as propensity score methods, G-computation, and regression techniques. Targeted maximum likelihood estimation (TMLE) is an alternative method that allows the use of data-adaptive and machine learning algorithms for model fitting. TMLE therefore does not require strict assumptions about the model specification but preserves the validity of the inference. Multiple studies have shown that TMLE outperforms other methods in certain real-world settings, making it a useful and potentially superior algorithm for causal inference. However, there is a lack of accessible resources for practitioners to understand the implementation. Hence the TMLE framework is the least-used method by practitioners in epidemiology literature. Recently a few accessible articles have been published, but they focus only on binary outcomes and demonstrations are done mainly with simulated data. This paper aims to fill the gap in the literature by providing a step-by-step TMLE implementation guide for a continuous outcome, using an openly accessible clinical dataset.
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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 | 0.020 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".