So sánh phương pháp

	Phân tích Tác động Nhân quả của Hiệu ứng Điều trị Không đồng nhất	Ghép cặp điểm xu hướng
Lĩnh vực	Suy luận nhân quả	Thống kê nghiên cứu
Họ	Regression model	Process / pipeline
Năm ra đời	2015-2016	1983
Người khởi xướng	Brodersen et al. (causal impact framework, 2015); Athey & Imbens (HTE estimation, 2016)	Paul Rosenbaum and Donald Rubin
Loại	Causal inference / heterogeneous effects estimation	Method
Công trình gốc	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.	Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.
Tên gọi khác	HTE-CausalImpact, CATE causal impact, heterogeneous causal impact, subgroup causal impact analysis	PSM, propensity score weighting, covariate balance
Liên quan	5	3
Tóm tắt	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.	Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias.