Causal Inference with Observational Data via Propensity Score Matching: A Simple, Hands‑On Guide
Bibliographic record
Abstract
Propensity score matching (PSM) was presented as a practical method for making fair comparisons in studies without random assignment. This paper explained PSM in accessible terms and provided a step-by-step workflow that could be followed by readers. The research was conducted to address the issue of differences between treated and untreated individuals. The key ideas behind PSM, the assumptions required for it to work, and approaches to verifying these assumptions with simple plots and summary statistics were described. Furthermore, the main steps were outlined: defining the target question, selecting baseline covariates, estimating the propensity score, performing matching or reweighting, checking balance, estimating the treatment effect, and conducting basic sensitivity analyses. To maintain a hands-on perspective, a small numerical example and a concise checklist were included to support replication in other studies. Besides, common pitfalls were discussed, such as weak overlap, overly strict calipers, and post-matching analyses that ignored the matched structure. This paper aimed to help students and applied researchers develop PSM analyses that were transparent, reproducible, and easy to communicate to non-specialists.
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 | 0.039 | 0.118 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.042 | 0.024 |
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".