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To solve this dimensionality problem, PSM estimates the propensity score, that is, the probability of participating in a program conditional on X.
Following [6, 45, 46] the paper estimates the propensity scores on which women in the sample are matched into and put in two groups-women with valid health insurance and women without valid health insurance.
Second, we used a selection model that estimates the propensity of trial results being published and in which trials with lower propensity are weighted up in the NMA model.
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Generalized boosting, a non-parametric modeling technique, was used to estimate the propensity scores.
The first step in estimating the treatment effect is to estimate the propensity score.
Specifically, we first estimate a Probit model to estimate the propensity of hiring returnee managers.
The results of the bivariate probit model estimating the propensity scores are given in Table 6.
After implementing the logit model for cooperative membership, we estimated the propensity scores.
There are however two ways of incorporating sampling weights when estimating the propensity score.
Instead, we estimate the propensity score based on covariates Z. Specifically, begin{aligned} hat{e}(Z) = p(T = 1|Z), end{aligned} (9 which is referred to as the estimated propensity score.
This sub-section presents the result of the probit regression model, which was used to estimate the propensity score for matching the off-farm participants with non-participants.
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