A Regression-with-Residuals Method for Estimating Controlled Direct Effects
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Bibliographic record
Abstract
Political scientists are increasingly interested in causal mediation, and to this end, recent studies focus on estimating a quantity called the controlled direct effect (CDE). The CDE measures the strength of the causal relationship between a treatment and outcome when a mediator is fixed at a given value. To estimate the CDE, Joffe and Greene (2009) and Vansteelandt (2009) developed the method of sequential g-estimation, which was introduced to political science by Acharya, Blackwell, and Sen (2016). In this letter, we propose an alternative method called “regression-with-residuals” (RWR) for estimating the CDE. In special cases, we show that these two methods are algebraically equivalent. Yet, unlike sequential g-estimation, RWR can easily accommodate several types of effect moderation, including cases in which the effect of the mediator on the outcome is moderated by a posttreatment confounder. Although common in the social sciences, this type of effect moderation is typically assumed away in applications of sequential g-estimation, which may lead to bias if effect moderation is in fact present. We illustrate RWR by estimating the CDE of negative media framing on public support for immigration, controlling for respondent anxiety.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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