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Reka Bentuk Pecahan Regresi (RDD)×Kaedah Padanan (CEM / Optimal / Genetik)×
BidangInferens KausalInferens Kausal
KeluargaRegression modelRegression model
Tahun asal20082012
PengasasImbens & Lemieux (guide to practice); Cattaneo, Idrobo & Titiunik (practical introduction)Iacus, King & Porro (CEM); Hansen (optimal/full matching)
JenisQuasi-experimental causal designMatching for causal inference
Sumber perintisImbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635. DOI ↗Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24. DOI ↗
AliasRDD, regression discontinuity design, sharp RDD, fuzzy RDDcoarsened exact matching, optimal matching, genetic matching, CEM
Berkaitan55
RingkasanRegression Discontinuity Design is a quasi-experimental method that identifies a causal effect by locally comparing units just above and just below a cutoff on a continuous assignment (running) variable. Formalised for applied work by Imbens and Lemieux (2008) and developed as a practical framework by Cattaneo, Idrobo, and Titiunik (2020), it estimates a local average treatment effect (LATE) at the threshold.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.
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ScholarGateBandingkan kaedah: Regression Discontinuity · Matching Methods. Dicapai 2026-06-17 daripada https://scholargate.app/ms/compare