ScholarGate
アシスタント

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。

粗化完全マッチング(CEM)×差分の差 (Difference-in-Differences, DiD)×
分野因果推論計量経済学
系統Regression modelRegression model
提唱年2011-20121994
提唱者Iacus, King, & PorroCard & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
種類Matching / causal inferenceCausal inference / panel regression
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 ↗Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
別名CEM, coarsened matching, monotonic imbalance bounding matchingdiff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
関連65
概要Coarsened Exact Matching is a preprocessing method that achieves covariate balance by temporarily coarsening continuous variables into bins, exactly matching treated and control units within those bins, and then discarding all unmatched units. Introduced by Iacus, King, and Porro (2011, 2012), it bounds imbalance on each covariate independently, yielding a matched sample on which any estimator can be applied without relying on a propensity score model.Difference-in-Differences is a causal-inference method that estimates the effect of an intervention by comparing how a treatment group and a control group change over time. Made famous by Card and Krueger's 1994 minimum-wage study and developed in Angrist and Pischke's Mostly Harmless Econometrics, it isolates the treatment effect as the difference between the two groups' before-after changes.
ScholarGateデータセット
  1. v1
  2. 2 出典
  3. PUBLISHED
  1. v1
  2. 2 出典
  3. PUBLISHED

検索へ スライドをダウンロード

ScholarGate手法を比較: Coarsened Exact Matching · Difference-in-Differences. 2026-06-17に以下より取得 https://scholargate.app/ja/compare