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The phrase "inverse probability weighting" is correct and can be used in written English.
It is a statistical concept that refers to a method of adjusting for sample selection bias in a study. Example: "The researchers applied inverse probability weighting techniques to account for potential bias in their sample, resulting in more accurate and reliable findings."
Exact(60)
Existing approaches in handling missing data include likelihood, imputation and inverse probability weighting.
An inverse probability weighting approach for propensity score was also considered as sensitivity analysis.
However, we also performed an analysis based on inverse probability weighting.
Finally, we performed a sensitivity analysis using the inverse probability weighting (IPW) approach to estimate the treatment effect.
Second, under certain assumptions, the inverse probability weighting estimator achieves the semi-parametric efficiency bound derived by Hahn (1998).
It is nevertheless possible to obtain consistent estimates using the inverse probability weighting estimator, as suggested by Wooldridge (2007).
The propensity analysis as well as the inverse probability weighting approach suggests that few biases explain this lack of benefit.
The small number of observed cases precluded additional levels of stratification for the inverse probability weighting.
Inverse probability weighting.
Inverse probability weighting (IPW) adjusted estimates.
Data adjusted with the use of inverse probability weighting.
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CEO of Professional Science Editing for Scientists @ prosciediting.com