השוואת שיטות
סקרו את השיטות שבחרתם זו לצד זו; שורות שבהן יש הבדל מודגשות.
| מַעֲרִיך הַתְאָמַת הַעֲרָכַת מְדִינִיּוֹת× | התאמת ציון נטייה× | |
|---|---|---|
| תחום≠ | הסקה סיבתית | סטטיסטיקה למחקר |
| משפחה≠ | Regression model | Process / pipeline |
| שנת המקור≠ | 1998-2006 | 1983 |
| הוגה השיטה≠ | Heckman, Ichimura & Todd; Abadie & Imbens | Paul Rosenbaum and Donald Rubin |
| סוג≠ | Non-parametric causal estimator | Method |
| מקור מכונן≠ | Abadie, A., & Imbens, G. W. (2006). Large sample properties of matching estimators for average treatment effects. Econometrica, 74(1), 235-267. DOI ↗ | Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55. DOI ↗ |
| כינויים≠ | matching estimator, program evaluation matching, treatment effect matching, Abadie-Imbens estimator | PSM, propensity score weighting, covariate balance |
| קשורות≠ | 6 | 3 |
| תקציר≠ | The policy evaluation matching estimator estimates the causal effect of a program or policy on treated units by pairing each participant with one or more non-participants who share similar pre-treatment characteristics. Developed rigorously by Heckman, Ichimura & Todd (1998) and Abadie & Imbens (2006), it avoids parametric outcome models and is the standard non-parametric tool for program and policy evaluation. | Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias. |
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