This paper demonstrates how modeling and including the latent variable in the propensity score matching can improve the quality of the treatment estimates in comparison

being able to quantify the net benefit of making that choice, we may employ a generalization of the binary choice framework to model an ordinal variable using ordered probit or

use _times as frequency weights to identify the matched treated and the (possibly repeatedly) matched controls. 2) _ matchdif → pairwise difference

Conclusion 3: Healthful food and beverage choices are available to students in North Carolina’s school breakfast and lunch program; conducting nutrient analyses of school meals and

Obtained genetic trend in milk yield for cows and corresponding regression lines with equation showing annual response using a single-lactation model (black lines) or a

Using data from the Swiss Household Panel and propensity score matching models, I find that low propensity men – after controlling for labor market variables – benefit most from

working example for the illustration purpose; (II) PSM for the simulated dataset; (III) standardized mean difference (SMD) for assessing covariate balance after matching; (IV)

They are from: (1) true outcome regression; (2) stratification by five strata; (3) matching with caliper 0.1; (4) matching with caliper 0.2, the optimal caliper; (5) matching

In spite of the great popularity that propensity score matching methods have gained since they were proposed by Rosenbaum and Rubin in 1983, their large sample distribution has not

Yet, using Monte Carlo simulations this paper shows that the efficiency in estimation of the ATT can be gained if all the variables in the outcome equation including those not

Propensity score matching is a widely-used method to measure the effect of a treatment in.. social as well as

• The SNMPv1 SMI specifies that all managed objects have a certain subset of Abstract Syntax Notation One (ASN.1) data types associated with them.. • Three ASN.1 data types

(2003) Efficient estimation of average treatment effects using the estimated propensity score. (1976) On a non-parametric analogue of the

Estimators that adjust for the propensity score in a nonparametric way, such as matching on an estimate of the propensity score, can be consistent when the es- timated propensity

Table 3 in the Appendix presents mean values of the variables underlying the propensity score matching before the matching took place. Mean characteristics of workers taking

FORM 990, PART I, LINE 1, DESCRIPTION OF ORGANIZATION MISSION: PERISHABLE FOOD THAT WOULD OTHERWISE BE WASTED AND DELIVERS IT TO ORGANIZATIONS THAT SERVE THE HUNGRY IN BERGEN,

Our matching procedure corrected for coverage error, as a result of the matching procedures both matched samples were equal on all variables included in the propensity score.. But the

Propensity score matching with clustered data in Stata..