手法を比較
選択した手法を並べて確認できます。異なる行はハイライト表示されます。
| マッチング手法（CEM / 最適 / ジェネティック）× | 逆確率重み付け法 (IPW / IPTW)× | |
|---|---|---|
| 分野 | 因果推論 | 因果推論 |
| 系統 | Regression model | Regression model |
| 提唱年≠ | 2012 | 2000 |
| 提唱者≠ | Iacus, King & Porro (CEM); Hansen (optimal/full matching) | Robins, Hernán & Brumback |
| 種類≠ | Matching for causal inference | Causal inference weighting estimator |
| 原典≠ | 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 ↗ |
| 別名 | coarsened exact matching, optimal matching, genetic matching, CEM | IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting |
| 関連 | 5 | 5 |
| 概要≠ | Matching Methods are a family of causal-inference techniques beyond propensity-score matching that pair treated and control units with similar covariates so that a treatment effect can be read off the balanced sample. The family includes Coarsened Exact Matching (Iacus, King & Porro, 2012), optimal matching, and genetic matching. | 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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