Statistical simulation and counterfactual analysis in social sciences
Bibliographic record
Abstract
In this paper, we present statistical simulation techniques of interest in substantial interpretation of regression results. Taking stock of recent literature on causality, we argue that such techniques can operate within a counterfactual framework. To illustrate, we report findings using post-electoral data on voter turnout. The analysis of quantitative data, and the estimation of regression models in particular, can now be considered commonplace in the social sciences. There are, of course, notable variations in the ways those analyses are generated (research design, estimation methods, etc.). In the same way, there are discrepancies in terms of standards when it comes to the interpretation of the results and their proper communication. Depending on the nature of the data at hand and the chosen estimation methods, the interpretation phase can be rather equivocal. For instance, displaying the odd ratios, or their natural logarithm, following logistic regressions can be far from intelligible, especially when one is interested in parameters beyond their statistical significance threshold and the direction of their coefficients. Consequently, a greater analytical effort appears to be required to flesh out the proper signification and meaning of parameters, most particularly to express their magnitude. The interpretation of statistical results appears crucial, especially under the lens of knowledge transfer, which involves non-statistical experts (decision-makers, policy analysts, etc.). We contend here that statistical simulation can be put to profit to this end. We also make the argument that this approach is compatible with a counterfactual conception of causality. Although this is not the place to develop a full-fledged
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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 | 0.032 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".