Machine learningCausal ML
Targeted Maximum Likelihood Estimation (TMLE)
Targeted Maximum Likelihood Estimation (TMLE) 是一种半参数、双重稳健的因果推断方法，由 Mark van der Laan 和 Daniel Rubin 于 2006 年提出。它结合了用于结果和处理分配机制的灵活机器学习模型，然后应用一个目标化步骤，该步骤重新拟合初始结果模型，专门用于减少预定因果估计量（如平均处理效应）的偏差。在从观察性数据估计因果效应时，TMLE 在流行病学、生物统计学和卫生经济学中被广泛使用。
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来源
- van der Laan, M. J., & Rubin, D. (2006). Targeted maximum likelihood learning. The International Journal of Biostatistics, 2(1). DOI: 10.2202/1557-4679.1043 ↗
如何引用本页
ScholarGate. (2026, June 2). Targeted Maximum Likelihood Estimation (TMLE). ScholarGate. https://scholargate.app/zh/causal-inference/targeted-maximum-likelihood
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