ScholarGate
Msaidizi
Regression modelQuasi-experimental / causal inference

Uchambuzi wa Athari ya Kifahili

Uchambuzi wa Athari ya Kifahili, ulioanzishwa na Brodersen et al. (2015) katika kampuni ya Google, hutumia mifumo ya muda ya Bayesian ili kukadiria kile kingechotokea kwa matokeo iwapo uingiliaji ungekuwepo. Kwa kujenga kinyume cha ukweli cha uwezekano kutoka kwa data ya kabla ya matibabu na vigezo vya udhibiti, unatoa vipimo vya athari za matibabu kwa wakati na kwa jumla pamoja na vipindi kamili vya kutokuwa na uhakika vya baada.

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Vyanzo

  1. Brodersen, 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: 10.1214/14-AOAS788
  2. CausalImpact. Wikipedia. link

Jinsi ya kunukuu ukurasa huu

ScholarGate. (2026, June 3). Bayesian Structural Time-Series Causal Impact Analysis. ScholarGate. https://scholargate.app/sw/causal-inference/causal-impact-analysis

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Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.

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Imerejelewa na

ScholarGateCausal Impact Analysis (Bayesian Structural Time-Series Causal Impact Analysis). Imepatikana 2026-06-15 kutoka https://scholargate.app/sw/causal-inference/causal-impact-analysis · Seti ya data: https://doi.org/10.5281/zenodo.20539026