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删除3字节 、 2021年6月2日 (三) 11:10
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Let some subject have a vector of covariates ''X'' (i.e.: conditionally unconfounded), and some '''potential outcomes''' ''r''<sub>0</sub> and ''r''<sub>1</sub> under control and treatment, respectively. Treatment assignment is said to be '''strongly ignorable''' if the potential outcomes are [[statistical independence|independent]] of treatment (''Z'') conditional on background variables ''X''.  This can be written compactly as
 
Let some subject have a vector of covariates ''X'' (i.e.: conditionally unconfounded), and some '''potential outcomes''' ''r''<sub>0</sub> and ''r''<sub>1</sub> under control and treatment, respectively. Treatment assignment is said to be '''strongly ignorable''' if the potential outcomes are [[statistical independence|independent]] of treatment (''Z'') conditional on background variables ''X''.  This can be written compactly as
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设某个物体有向量协变量''X''(即:条件非混杂变量),以及'''潜在结果'''''r''<sub>0</sub>和''r''<sub>1</sub>分别对应着控制和处理两种情况。如果潜在结果在给定背景变量''X''的条件下独立于处理举动(''Z''),则可以说样本是否接受处理分配是'''强可忽略'''的。可简洁表述为
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设某个物体有协变量''X''(即:条件非混杂变量)向量,以及对应着控制和处理两种情况的'''潜在结果'''''r''<sub>0</sub>和''r''<sub>1</sub>。如果潜在结果在给定背景变量''X''的条件下独立于处理举动(''Z''),则可以说样本是否接受处理分配是'''强可忽略'''的。可简洁表述为
    
:<math> r_0, r_1 \perp Z \mid X </math>
 
:<math> r_0, r_1 \perp Z \mid X </math>
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