Sharpening blunt instruments: Exploiting non-linearities to enhance identification
Bibliographic record
Abstract
Without the ability to directly manipulate treatment assignment, scholars often turn to quasi-experimental identification strategies. Perhaps the most popular yet contentious approach has been to find a plausibly exogenous instrumental variable to estimate the treatment effect through two-stage least squares or two-stage residual inclusion. A common issue with this strategy is that potential instruments are often found to be weak, invalid, or both, in which case the identification strategy is inappropriate and the results potentially misleading. This note proposes an identification strategy with which non-linearities in the first stage can be exploited to (1) increase the strength of first-stage relationships (2) render otherwise invalid instruments valid, and (3) identify more than one treatment effect with the same source of exogenous variation. The approach is illustrated through simulations and is applied to the study of determinants of economic growth. R code is provided in the Appendix to facilitate its use in applied studies. Our proposed approach expands the domain of applicability for instrumental variables by loosening rather than imposing assumptions.
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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 | 0.071 | 0.246 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".