更改

'''Inverse probability weighting''' is a statistical technique for calculating statistics standardized to a [[pseudo-population]] different from that in which the data was collected. Study designs with a disparate sampling population and population of target inference (target population) are common in application.<ref name="refname2" /> There may be prohibitive factors barring researchers from directly sampling from the target population such as cost, time, or ethical concerns.<ref name="refname3" /> A solution to this problem is to use an alternate design strategy, e.g. [[stratified sampling]]. Weighting, when correctly applied, can potentially improve the efficiency and reduce the bias of unweighted estimators.

 

'''Inverse probability weighting''' is a statistical technique for calculating statistics standardized to a [[pseudo-population]] different from that in which the data was collected. Study designs with a disparate sampling population and population of target inference (target population) are common in application.<ref name="refname2"/> There may be prohibitive factors barring researchers from directly sampling from the target population such as cost, time, or ethical concerns.<ref name="refname3"/> A solution to this problem is to use an alternate design strategy, e.g. [[stratified sampling]]. Weighting, when correctly applied, can potentially improve the efficiency and reduce the bias of unweighted estimators.

Inverse probability weighting is a statistical technique for calculating statistics standardized to a pseudo-population different from that in which the data was collected. Study designs with a disparate sampling population and population of target inference (target population) are common in application. There may be prohibitive factors barring researchers from directly sampling from the target population such as cost, time, or ethical concerns. A solution to this problem is to use an alternate design strategy, e.g. stratified sampling. Weighting, when correctly applied, can potentially improve the efficiency and reduce the bias of unweighted estimators.

Inverse probability weighting is a statistical technique for calculating statistics standardized to a pseudo-population different from that in which the data was collected. Study designs with a disparate sampling population and population of target inference (target population) are common in application. There may be prohibitive factors barring researchers from directly sampling from the target population such as cost, time, or ethical concerns. A solution to this problem is to use an alternate design strategy, e.g. stratified sampling. Weighting, when correctly applied, can potentially improve the efficiency and reduce the bias of unweighted estimators.

当缺失数据不能包含在初步分析中时，逆概率加权也可用于考虑缺失数据。根据抽样概率的估计，或在另一测量中测量该因素的概率，可以使用逆概率加权来夸大由于大量数据缺失而代表性不足的受试者的权重。

当缺失数据不能包含在初步分析中时，逆概率加权也可用于考虑缺失数据。根据抽样概率的估计，或在另一测量中测量该因素的概率，可以使用逆概率加权来夸大由于大量数据缺失而代表性不足的受试者的权重。

== Inverse Probability Weighted Estimator (IPWE) ==

== 逆概率加权估计量(Inverse Probability Weighted Estimator, IPWE) ==

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.