قارن الطرق
راجع الطرق التي اخترتها جنبًا إلى جنب؛ الصفوف المختلفة مميَّزة.
| تقييم السياسات عبر المطابقة التامة التقريبية (CEM)× | الترجيح الاحتمالي العكسي (IPW / IPTW)× | |
|---|---|---|
| المجال | الاستدلال السببي | الاستدلال السببي |
| العائلة | Regression model | Regression model |
| سنة النشأة≠ | 2011-2012 | 2000 |
| صاحب الطريقة≠ | Iacus, King & Porro | Robins, Hernán & Brumback |
| النوع≠ | Matching / quasi-experimental design | Causal inference weighting estimator |
| المصدر التأسيسي≠ | Iacus, S. M., King, G., & Porro, G. (2012). Causal inference without balance checking: Coarsened exact matching. Political Analysis, 20(1), 1-24. DOI ↗ | Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560. DOI ↗ |
| الأسماء البديلة≠ | CEM, Coarsened Exact Matching, CEM policy evaluation, coarsening-based matching | IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting |
| ذات صلة | 5 | 5 |
| الملخص≠ | Coarsened Exact Matching (CEM) is a quasi-experimental causal-inference technique that creates balanced treatment and control groups from observational data by temporarily coarsening covariates into bins, exactly matching units within those bins, and then pruning unmatched observations before estimating policy effects. Introduced by Iacus, King, and Porro, CEM belongs to the monotonic imbalance bounding family of matching methods and is especially popular in policy evaluation. | Inverse Probability Weighting is a causal-inference method that assigns each observation a weight equal to the inverse of its probability of receiving the treatment it actually received. Introduced by Robins, Hernán and Brumback (2000) for marginal structural models, it builds a pseudo-population in which treatment is independent of measured confounders, balancing selection bias. |
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