Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Mfululizo wa Wakati Uliokatizwa Ulioimarishwa na Akili Bandia× | Uchambuzi wa Athari ya Kifahili× | |
|---|---|---|
| Nyanja | Uhitimisho wa Kisababishi | Uhitimisho wa Kisababishi |
| Familia | Regression model | Regression model |
| Mwaka wa asili≠ | 2014-2015 | 2015 |
| Mwanzilishi≠ | Brodersen et al. (2015); Varian (2014) — foundational ML-for-causal-inference literature | Kay H. Brodersen, Fabian Gallusser, Jim Koehler, Nicolas Remy, Steven L. Scott (Google) |
| Aina≠ | Quasi-experimental causal inference with ML counterfactual | Bayesian causal inference / counterfactual forecasting |
| Chanzo asilia | 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 ↗ | 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 ↗ |
| Majina mbadala | ML-ITS, ML-augmented ITS, machine learning ITS, causal ML interrupted time series | CausalImpact, BSTS causal inference, Bayesian causal impact, counterfactual time-series analysis |
| Zinazohusiana≠ | 6 | 5 |
| Muhtasari≠ | Machine Learning-Augmented Interrupted Time Series (ML-ITS) estimates the causal effect of a discrete intervention by training a machine learning model on pre-intervention time series data, projecting a counterfactual trajectory into the post-intervention period, and measuring the gap between observed and predicted outcomes. It extends classical ITS by replacing parametric trend assumptions with flexible ML estimators such as gradient boosting, random forests, or Bayesian structural time-series models. | Causal 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. |
| ScholarGateSeti ya data ↗ |
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