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）× | 隠れたバイアスに対する感度分析（ローゼンバウム限界値 / E値）× | |
|---|---|---|
| 分野 | 因果推論 | 因果推論 |
| 系統 | Regression model | Regression model |
| 提唱年≠ | 2012 | 2002 |
| 提唱者≠ | Iacus, King & Porro (CEM); Hansen (optimal/full matching) | Paul R. Rosenbaum (bounds); Tyler J. VanderWeele & Peng Ding (E-value) |
| 種類≠ | Matching for causal inference | Sensitivity analysis for causal inference |
| 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 ↗ | Rosenbaum, P. R. (2002). Observational Studies (2nd ed.). Springer. ISBN: 978-0387989679 |
| 別名 | coarsened exact matching, optimal matching, genetic matching, CEM | Rosenbaum bounds, E-value, hidden bias sensitivity analysis, unmeasured confounding sensitivity |
| 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. | Sensitivity analysis for hidden bias is a family of methods that quantify how strongly an unmeasured confounder would have to operate before it could overturn a causal conclusion drawn from observational data. It was crystallised by Paul Rosenbaum's sensitivity bounds (2002) and extended by VanderWeele and Ding's E-value (2017). |
| ScholarGateデータセット ↗ |
|
|