search for: propensity

Dear All, My question is more a statistical question than a R question. The reason I am posting here is that there are lots of excellent statistician on this list, who can always give me good advices. Per my understanding, the purpose of propensity score is to reduce the bias while estimating the treatment effect and its implementation is a 2-stage model. 1) First of all, if we assume that T = 1 if an individual belongs to treatment group and T = 0 otherwise. We further assume that X is a covariate matrix to explain the assignment of treatme...

Hi all, i am looking to built a simple example of a very basic propensity score adjustment, just using the estimated propensity scores as inverse probability weights (respectively 1-estimated weights for the non-treated). As far as i understood, MLE predictions of a logit model can directly be used as to estimates of the propensity score. I already considered the...

Dear All, I would like to find something ( references, code,..) to implement a comparison of three treatments in an observational study using the 'Propensity Score'. Any help is much appreciated. Thanks! Giovanni -- dr. Giovanni Parrinello Department of Biotecnologies Medical Statistics Unit University of Brescia Viale Europa, 11 25123 Brescia email: parrinel at med.unibs.it Phone: +390303717528 Fax: +390303717488

...have 6 different sets of samples. Each sample has about 5000 observations, with each observation comprised of 150 baseline covariates (X), 125 of which are dichotomous. Roughly 20% of the observations in each sample are "treatment" and the rest are "control" units. i am doing propensity score matching, i have already estimated propensity scores(predicted probabilities) using logistic regression, and in each sample i am going to have to exclude approximately 100 treated observations for which I cannot find matching control observations (because the scores for these treated units ar...

...ibe. I have been struggling with the same error message for a while, and I can't seem to get past it. Here is the issue: I am using a data set that uses -1:-9 to indicate various kinds of missing data. I changed all of these to NA, regardless of the cause of the missing data. I am trying to do propensity score matching with this data, but it will not calculate the propensity scores, regardless of which method I have tried. I have tried the following methods: 1. Optimal propensity score matching, using the MatchIt library: m.out<-matchit(assignment~totalexp + yrschool+new+cert+age+STratio + percm...

...f such methods. Machine learning methods that yield calibrated probability estimates should do well (e.g. those that optimize the logistic log-likelihood). Methods that only seek a decision boundary (SVM comes to mind) can be give great classifiers but offer poor probability estimates and then the propensity score weights are a mess. We've had a lot of success in practice using gbm and selecting the number of iterations to optimize balance. You can try the ps() function in the twang package which wraps up gbm and balance optimization in a single function. It's slow for large datasets but it get...

I'm using the "Match" package to do propensity score matching. Here's some example code that shows the problem that I'm having (much of this code is taken from the Match package documentation): *data(lalonde) glm1 <- glm(treat~age + I(age^2) + educ + I(educ^2) + black + hisp + married + nodegr + re74 + I(re74^2) + re7...

Hi. I am using the Matching package for propensity score matching. For each treated unit, I want to find all control units whose propensity scores lie within a certain distance from the treated unit. The sample code is as follows: > library(Matching) > x <- rnorm(100000) > y <- rnorm(100000) > z <- rbinom(100000,1,0.002) &gt...

"Matching" version 0.48 is now available on CRAN. Matching provides functions for estimating causal effects by multivariate and propensity score matching. The package includes a variety of univariate and multivariate tests to determine if balance has been obtained by the matching procedure. These tests can also be used to determine if an experiment or quasi-experiment is balanced on baseline covariates. The functions provide valid st...

"Matching" version 0.48 is now available on CRAN. Matching provides functions for estimating causal effects by multivariate and propensity score matching. The package includes a variety of univariate and multivariate tests to determine if balance has been obtained by the matching procedure. These tests can also be used to determine if an experiment or quasi-experiment is balanced on baseline covariates. The functions provide valid st...

Hi all, i am wondering if there?s any other method to adjust for selection related bias of estimates except propensity scoring and heckit / mills ratio approach? i also read documentation of Match and twang package so far, so i don?t speak of any ATE / ATT related methods, respectively any methods that match or stratify... Is there something else ? thx in advance

Dear List, I am having difficulty running the ps() function when variables are stored as factors and was hoping someone could provide some advice on how to proceed. I am running propensity score matching as outlined in: Greg Ridgeway, Dan McCarey, Andrew Morral, Lane Burgette and Beth Ann Grin (May 3, 2013) Toolkit for Weighting and Analysis of Nonequivalent Groups: A tutorial for the twang package and have a question about using unordered categorical variables as a covariates. Th...

Hola chic en s, alguien con experiencia en propensión score matching? Planteo duda: Clasicamente el PSM se ha utilizado en un intento de homogeneizar cohortes de enfermos quienes han estado ?expuestos? a un tratamiento x Vs aquellos que no han estado expuestos (no expuestos). Esto aplica para medicamentos o procedimientos quirúrgicos o no. Bien, En algún articulo he leído que el PSM se puede

Hola chic en s, alguien con experiencia en propensión score matching? Planteo duda: Clasicamente el PSM se ha utilizado en un intento de homogeneizar cohortes de enfermos quienes han estado ?expuestos? a un tratamiento x Vs aquellos que no han estado expuestos (no expuestos). Esto aplica para medicamentos o procedimientos quirúrgicos o no. Bien, En algún articulo he leído que el PSM se puede

Hi! I'm trying to perform propensity score matching on survey data and so for each individual observation I have a statistical weight attached. My question is: is there a way within the package to consider these weights in the matching procedure? Thank you very much. -- Maria Cristina Maurizio [[alternative HTML version deleted]]

Hello all. I am doing one part of an evaluation of a mandatory welfare-to-work programme in the UK. As with all evaluations, the problem is to determine what would have happened if the initiative had not taken place. In our case, we have a number of pilot areas and no possibility of random assignment. Therefore we have been given control areas. My problem is to select for survey individuals in

...t base on the observational data.And the treatment variable is multinomial rather than binary.Because the treatment assignment is not random,so the selection-bias exists.Under this condition,what's the best way to estimate the treatment effect? I know that if the treatment is binary,I can use propensity score matching using MatchIt package.But what about multinomial? Maybe one way is the method proposed by Imbens,2000,The role of the propensity score in estimating dose-response functions,Biometrika,87:706-710.But I can't find any R function to do the task.So I hope the lister can give me som...

...10, 100) > x3 = rnorm(n, 5, 1) > x4 = rnorm(n, 10, 10) > x5 = rbinom(n, 1, .4) > x6 = rnorm(n, 30, 5) > treatment = rbinom(n, 1, .15) > data = cbind(x1,x2,x3,x4,x5,x6, treatment) > dim(data) > > ################################################################ > ###### Propensity score matching > ###### nearest neighbor matching (1:1) > ################################################################ > > require(MatchIt) > # data1 is the subset of data with only the selected variables mentioned > below > data1 = data[,c("x1","x2",&q...

Hello , My optmatch package is loaded on R and otherwise running fine. I get an error after lcds successfully completes a logistic regression and I then try to obtain a propensity score: pdist <- pscore.dist(lcds) Error: could not find function "pscore.dist" Does anyone know if pscore.dist is part of the optmatch package, or is it contained in another package that I need to load? I searched the help files, other online sources, could find no answer for t...

Dear list, I am trying to set up a propensity-weighted regression using the survey package. Most of my population is sampled with a sampling probability of one (that is, I have the full population). However, for a subset of the data I have only a 50% sample of the full population. In previous work on the data, I analyzed these data using SAS a...