Causal Inference for Time-Varying Instructional Treatments
Bibliographic record
Abstract
The authors propose a strategy for studying the effects of time-varying instructional treatments on repeatedly observed student achievement. This approach responds to three challenges: (a) The yearly reallocation of students to classrooms and teachers creates a complex structure of dependence among responses; (b) a child’s learning outcome under a certain treatment may depend on the treatment assignment of other children, the skill of the teacher, and the classmates and teachers encountered in the past years; and (c) time-varying confounding poses special problems of endogeneity. The authors address these challenges by modifying the stable unit treatment value assumption to identify potential outcomes and causal effects and by integrating inverse probability of treatment weighting into a four-way value-added hierarchical model with pseudolikelihood estimation. Using data from the Longitudinal Analysis of School Change and Performance, the authors apply these methods to study the impact of “intensive math instruction” in Grades 4 and 5.
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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 | 0.161 | 0.456 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.019 | 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; 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".