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第10行： − The inverse probability weighting estimator can be used to demonstrate causality when the researcher cannot conduct a controlled experiment but has observed data to model. Because it is assumed that the treatment is not randomly assigned, the goal is to estimate the counterfactual or potential outcome if all subjects in population were assigned either treatment. − The inverse probability weighting estimator can be used to demonstrate causality when the researcher cannot conduct a controlled experiment but has observed data to model. Because it is assumed that the treatment is not randomly assigned, the goal is to estimate the counterfactual or potential outcome if all subjects in population were assigned either treatment. − 当研究人员不能进行控制实验，但有观测数据进行模型时，逆概率加权估计量可用于证明因果关系。因为假设治疗不是随机分配的，目标是估计反事实或潜在的结果，如果人口中的所有受试者被分配任何一种治疗。+
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== 逆概率加权估计量(Inverse Probability Weighted Estimator, IPWE) ==
== 逆概率加权估计量(Inverse Probability Weighted Estimator, IPWE) ==
当研究人员不能进行控制实验，但有观测数据进行建模时，逆概率加权估计量可用于证明因果关系。因为假设治疗不是随机分配的，如果总体中的所有受试者被分配了任何一种治疗，则目标是估计反事实或潜在结果。
Suppose observed data are <math>\{\bigl(X_i,A_i,Y_i\bigr)\}^{n}_{i=1}</math> drawn [[Independent and identically distributed random variables|i.i.d (independent and identically distributed)]] from unknown distribution P, where
Suppose observed data are <math>\{\bigl(X_i,A_i,Y_i\bigr)\}^{n}_{i=1}</math> drawn [[Independent and identically distributed random variables|i.i.d (independent and identically distributed)]] from unknown distribution P, where