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Аналіз причинно-наслідкового впливу×Різниця різниць (Diff-in-Diff)×
ГалузьПричинно-наслідковий висновокЕконометрика
РодинаRegression modelRegression model
Рік появи20151994
Автор методуKay H. Brodersen, Fabian Gallusser, Jim Koehler, Nicolas Remy, Steven L. Scott (Google)Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
ТипBayesian causal inference / counterfactual forecastingCausal inference / panel regression
Основоположне джерелоBrodersen, K. H., Gallusser, F., Koehler, J., Remy, N., & Scott, S. L. (2015). Inferring causal impact using Bayesian structural time-series models. Annals of Applied Statistics, 9(1), 247-274. DOI ↗Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
Інші назвиCausalImpact, BSTS causal inference, Bayesian causal impact, counterfactual time-series analysisdiff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Пов'язані55
ПідсумокCausal Impact Analysis, introduced by Brodersen et al. (2015) at Google, uses Bayesian structural time-series models to estimate what would have happened to an outcome had an intervention never occurred. By constructing a probabilistic counterfactual from pre-treatment data and control covariates, it quantifies point-in-time and cumulative treatment effects with full posterior uncertainty intervals.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.
ScholarGateНабір даних
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ScholarGateПорівняння методів: Causal Impact Analysis · Difference-in-Differences. Отримано 2026-06-15 з https://scholargate.app/uk/compare