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1. Keep their weight trim.
We need to embrace just three simple rules to lose weight, trim down and be healthier.
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That may be, but there is more than just the weight trimming issue at work.
The performance of boosted CART and random forests without weight trimming was similar to the best performance obtainable by weight trimmed logistic regression estimated propensity scores.
In contrast, weight trimming did not improve the performance of boosted CART and random forests.
Although common in the survey sampling world, weight trimming has not been investigated as thoroughly in propensity score settings.
The amount of trimming was also crucial: for all methods and scenarios, weight trimming beyond the optimal level substantially increased bias.
The performance of boosted CART and random forests without weight trimming was similar to the best possible performance obtained by logistic regression with trimming.
In various simulation scenarios, weight trimming had the potential to improve the performance of propensity score weights, in particular for logistic regression-estimated weights.
We suggest that analysts should focus attention on improving propensity score model specification and rely less on weight trimming to optimize propensity score weighting.
To better isolate the effects of weight trimming, we do not perform 'doubly robust' regression adjustment for covariates after weighting is applied [30].
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CEO of Professional Science Editing for Scientists @ prosciediting.com