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| Spatial Coarsened Exact Matching (Spatial CEM)× | Perbezaan-dalam-Perbezaan (Diff-in-Diff)× | |
|---|---|---|
| Bidang≠ | Inferens Kausal | Ekonometrik |
| Keluarga | Regression model | Regression model |
| Tahun asal≠ | 2012 (CEM foundation); spatial extension in applied literature 2015-present | 1994 |
| Pengasas≠ | Iacus, King & Porro (CEM foundation, 2012); extended to spatial contexts by applied spatial econometricians | Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment) |
| Jenis≠ | Quasi-experimental matching estimator with spatial covariates | Causal inference / panel regression |
| Sumber perintis≠ | 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 |
| Alias≠ | Spatial CEM, Geographic CEM, Spatial exact matching, CEM with spatial covariates | diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff) |
| Berkaitan≠ | 6 | 5 |
| Ringkasan≠ | Spatial Coarsened Exact Matching applies the Coarsened Exact Matching framework to study designs involving geographic units — neighbourhoods, census tracts, municipalities, or grid cells. Covariates are coarsened into discrete bins and units are matched exactly on those bins, with spatial attributes (location, adjacency, geographic characteristics) incorporated as matching dimensions to control for spatial confounding. | 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. |
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