Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Muundo wa Kukatizwa kwa Regressheni (RDD)× | Nafasi za Kulinganisha (CEM / Kulinganisha Bora / Kulinganisha kwa Vinasaba)× | |
|---|---|---|
| Nyanja | Uhitimisho wa Kisababishi | Uhitimisho wa Kisababishi |
| Familia | Regression model | Regression model |
| Mwaka wa asili≠ | 2008 | 2012 |
| Mwanzilishi≠ | Imbens & Lemieux (guide to practice); Cattaneo, Idrobo & Titiunik (practical introduction) | Iacus, King & Porro (CEM); Hansen (optimal/full matching) |
| Aina≠ | Quasi-experimental causal design | Matching for causal inference |
| Chanzo asilia≠ | Imbens, 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 ↗ |
| Majina mbadala | RDD, regression discontinuity design, sharp RDD, fuzzy RDD | coarsened exact matching, optimal matching, genetic matching, CEM |
| Zinazohusiana | 5 | 5 |
| Muhtasari≠ | Regression 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. |
| ScholarGateSeti ya data ↗ |
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