Порівняння методів

	Аналіз причинного впливу гетерогенного ефекту лікування	Аналіз причинно-наслідкового впливу
Галузь	Причинно-наслідковий висновок	Причинно-наслідковий висновок
Родина	Regression model	Regression model
Рік появи	2015-2016	2015
Автор методу	Brodersen et al. (causal impact framework, 2015); Athey & Imbens (HTE estimation, 2016)	Kay H. Brodersen, Fabian Gallusser, Jim Koehler, Nicolas Remy, Steven L. Scott (Google)
Тип	Causal inference / heterogeneous effects estimation	Bayesian causal inference / counterfactual forecasting
Основоположне джерело	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.	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.
Інші назви	HTE-CausalImpact, CATE causal impact, heterogeneous causal impact, subgroup causal impact analysis	CausalImpact, BSTS causal inference, Bayesian causal impact, counterfactual time-series analysis
Пов'язані	5	5
Підсумок	Heterogeneous treatment effect causal impact analysis extends the Bayesian structural time-series causal impact framework to estimate not just the average effect of an intervention but how that effect varies across subgroups or individual units. By combining counterfactual prediction with conditional average treatment effect (CATE) estimation, it reveals which groups benefit most or least from an intervention.	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.