Do Medical Marijuana Laws Increase Marijuana Use? Replication Study and Extension
Bibliographic record
Abstract
PURPOSE: To replicate a prior study that found greater adolescent marijuana use in states that have passed medical marijuana laws, and extend this analysis by accounting for confounding by unmeasured state characteristics and measurement error. METHODS: We obtained state-level estimates of marijuana use from the 2002-2009 National Survey on Drug Use and Health. We used two-sample t-tests and random-effects regression to replicate previous results. We used difference-in-differences regression models to estimate the causal effect of medical marijuana laws on marijuana use, and simulations to account for measurement error. RESULTS: We replicated previously published results showing higher marijuana use in states with medical marijuana laws. Difference-in-differences estimates suggested that passing medical marijuana laws decreased past-month use among adolescents by 0.53 percentage points (95% CI: 0.03-1.02) and had no discernible effect on the perceived riskiness of monthly use. Models incorporating measurement error in the state estimates of marijuana use yielded little evidence that passing medical marijuana laws affects marijuana use. CONCLUSIONS: Accounting for confounding by unmeasured state characteristics and measurement error had an important effect on estimates of the impact of medical marijuana laws on marijuana use. We find limited evidence of causal effects of medical marijuana laws on measures of reported marijuana use.
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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 | 0.049 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".