Designing Randomized Experiments to Predict Unit-Specific Treatment Effects
Bibliographic record
Abstract
When evaluating a program or policy, a randomized experiment is typically designed to test a single confirmatory hypothesis about the average treatment effect, although subgroup and moderator effects may also be explored. The resulting average treatment effect estimate is then reported in research clearinghouses and used to inform policy and practice decisions for units not in the study. This use suggests that the purpose of these randomized trials is not only the testing of hypotheses, but rather the prediction of treatment effects for a broad set of units in a population. In this paper, we consider the optimal design of a randomized experiment focused on the prediction of unit-specific effects. We consider how different sampling processes and models affect the mean squared error of these predictions. The results indicate, for example, that problems of generalizability – differences between study samples and target populations – can greatly increase prediction error. We also identify the conditions under which the best unit-specific treatment effect is the average treatment effect estimate. Throughout, we use simple regression models to connect the predictive and hypothesis testing literatures and to provide implications for the design of randomized experiments.
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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 | 0.265 | 0.530 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".