Statistical inference in the presence of imputed survey data through regression trees and random forests
Bibliographic record
Abstract
Abstract Item nonresponse in surveys is usually handled through some form of imputation. In recent years, imputation through machine learning procedures has attracted a lot of attention in national statistical offices. However, little is known about the theoretical properties of the resulting point estimators in a survey setting. In this article, we study regression trees and random forests that provide flexible tools for obtaining imputed values. In a high‐dimensional framework allowing the number of predictors to diverge, we lay out a set of conditions for establishing the mean square consistency of regression trees and random forests imputed estimators of a finite population mean. We propose a novel variance estimator based on a ‐fold cross‐validation procedure. The proposed point and variance estimation are assessed through a simulation study in terms of bias, efficiency, and coverage rate of normal‐based confidence intervals. Finally, the choice of hyperparameters involved in random forest algorithms is investigated through theoretical and empirical work.
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How this classification was reachedexpand
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 | 0.003 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".