Balancing Treatment and Control Groups in Quasi‐Experiments: An Introduction to Propensity Scoring
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Bibliographic record
Abstract
Organizational and applied sciences have long struggled with improving causal inference in quasi‐experiments. We introduce organizational researchers to propensity scoring, a statistical technique that has become popular in other applied sciences as a means for improving internal validity. Propensity scoring statistically models how individuals in a quasi‐experiment have been assigned to conditions in order to estimate treatment effects among individuals with approximately equal probabilities of receiving the treatment. If propensity scores are created from relevant covariates, matching on the propensity score makes treatment assignment ignorable and approximates a true experimental design. We illustrate how matching on the propensity score can be applied by using 2 examples: examining the effects of online instruction and estimating the benefits of preparatory coaching for the SAT. In both cases, propensity‐scoring methods effectively reduced inequivalence between treatment and control groups on many variables. Propensity scoring stands out as a valuable technique capable of improving causal inference from many of organizational research's quasi‐experiments.
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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 | 0.000 | 0.000 |
| 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