Estimating a treatment effect under uncertainty with application to a high‐speed railway system
Bibliographic record
Abstract
Abstract This article proposes new treatment effect models and applies them to analyse the effect of high‐speed railways on population density. These models focus on the difference in treatment intensity across treated observations. The difference in treatment intensity occurs as a result of uncertainty over whether the treatment will be properly administered to observations. When evaluating large infrastructure, such as high‐speed railways, the introduction of infrastructure is considered as the treatment for municipalities. The treatment in such cases usually takes time and poses uncertainty whether it will be complete or not. To address such uncertainty, we extend the Roy model—a typical treatment effect model—under a reasonable assumption to enable it to incorporate observations that are treated but their treatment is not complete. Further, we allow the correlation between how these observations are determined and their outcomes and treatment assignments. This article also discusses the identification problem with respect to estimating model parameters. The proposed statistical models are applied to evaluate the effect of the Shinkansen, a high‐speed railway in Japan, on population density. The Canadian Journal of Statistics 42: 337–358; 2014 © 2014 Statistical Society of Canada
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".