Bayesian implementation of Targeted Maximum Likelihood Estimation for uncertainty quantification in causal effect estimation
用于因果效应不确定性量化的贝叶斯目标极大似然估计
Robust decision making involves making decisions in the presence of uncertainty and is often used in critical domains such as healthcare, supply chains, and finance. Causality plays a crucial role in decision-making as it predicts the change in an outcome (usually a key performance indicator) due to a treatment (also called an intervention). To facilitate robust decision making using causality, this paper proposes three Bayesian approaches of the popular Targeted Maximum…