The Modified Instrumental Variable (MIV) for Endogenous Instrumental Variables
Why this work is in the frame
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Bibliographic record
Abstract
Abstract This article introduces an estimator aimed at reducing the inconsistency of instrumental variable estimates when the instrument is not fully exogenous. The degree of improvement depends on how much the instrument deviates from exogeneity: if the exogenous component outweighs the endogenous one, the proposed estimator can fully correct the inconsistency. Importantly, when the instrument is only weakly exogenous, the Modified Instrumental Variables (MIV) estimator does not alter the estimates—an outcome that provides a useful diagnostic for assessing whether the instrument was truly exogenous. To demonstrate the practical utility of the method, we apply it in two empirical settings: (i) a Mincer earnings equation using quarter of birth as an instrument for years of education, and (ii) an earnings regression using Vietnam War draft lottery eligibility as an instrument for military service. The results indicate that the quarter of birth requires modification due to endogeneity, while the draft lottery behaves as an exogenous instrument, validating its use in causal inference.
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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 | 0.001 | 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 it