Random forests for individual treatment effect estimation with the R package ITERF
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Treatment effects often vary across individuals within a population. In contexts such as personalized medicine, it is crucial to accurately estimate treatment effects at the individual level. Random forests are among the most popular, versatile, and efficient statistical learning methods. This article introduces the R package ITERF, designed to estimate individual treatment effects using random forests across various settings. In particular, new methods to estimate the maximum treatment effect are introduced. METHODS AND RESULTS: The ITERF package provides methods for estimating treatment effects in two scenarios: (1) survival outcomes with right-censoring and a binary treatment, and (2) continuous outcomes with a continuous treatment. All methods are based on random forests. A simulation study demonstrates that the proposed methods for estimating the maximum treatment effect perform as expected and show considerable promise. An illustration, using real data, that explores the link between sleep duration and cognitive health in the elderly is given. CONCLUSION: The ITERF package offers a fast and user-friendly tool for estimating treatment effect measures using random forests, making it a valuable resource for researchers and practitioners in personalized treatment evaluation.
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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 | 0.027 | 0.097 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.083 | 0.035 |
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".