The Modified Instrumental Variable (MIV) for Endogenous Instrumental Variables
Bibliographic record
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.036 | 0.193 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".