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http://ftp.iza.org/dp1588.pdf
Propensity Score Matching∗ Propensity Score Matching (PSM) has become a popular approach to estimate causal treatment effects. It is widely applied when evaluating labour market policies, but empirical examples can be found in very diverse fields of study. Once the researcher has decided to
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4213057/
Apr 30, 2014 · Once a propensity score has been calculated for each observation, one must ensure that there is overlap in the range of propensity scores across treatment and comparison groups (called “common support”).Cited by: 383
https://www.stata.com/meeting/italy14/abstracts/materials/it14_grotta.pdf
Matching most popular propensity score based method we match subjects from the treatment groups by e(X) subjects who are unable to be matched are discarded from the analysis A.Grotta - R.Bellocco A review of propensity score in Stata
http://fmwww.bc.edu/EC-C/S2013/823/EC823.S2013.nn12.slides.pdf
Propensity score matching Basic mechanics of matching The matching criterion could be as simple as the absolute difference in the propensity score for treated vs. non-treated units. However, when the sampling design oversamples treated units, it has been found that matching on the log odds of the propensity score (p=(1 p)) is a superior criterion.
https://stats.stackexchange.com/questions/50635/do-we-need-overlap-common-support-in-case-of-a-parametric-regression
In case of non-parametric (semi-parametric) estimation (matching on X or on the propensity score) this assumption is crucial. However, I am wondering whether this assumption has to hold if I want to estimate the treatment effect in a parametric regression (e.g. a simple multivariate linear model fitted by OLS).
http://cega.berkeley.edu/assets/cega_events/31/Matching_Methods.ppt
PSM: Key Assumptions Key assumption: participation is independent of outcomes conditional on Xi This is false if there are unobserved outcomes affecting participation Enables matching not just at the mean but balances the distribution of observed characteristics across treatment and control Density 0 1 Propensity score Region of common support ...
http://www.bristol.ac.uk/media-library/sites/cmm/migrated/documents/prop-scores.pdf
Rosenbaum and Rubin (1983) proposed propensity score matching as a method to reduce the bias in the estimation of treatment e ects with observational data sets. These methods have become increasingly popular in medical trials and in the evaluation of economic policy interventions. Grilli and Rampichini (UNIFI) Propensity scores BRISTOL JUNE ...
https://www.researchgate.net/publication/4794420_PSMATCH2_Stata_Module_to_Perform_Full_Mahalanobis_and_Propensity_Score_Matching_Common_Support_Graphing_and_Covariate_Imbalance_Testing
PSMATCH2: Stata Module to Perform Full Mahalanobis and Propensity Score Matching, Common Support Graphing, and Covariate Imbalance Testing Article (PDF Available) · May 2003 with 7,083 Reads
https://thomasgstewart.github.io/propensity-score-matching-in-stata/
Propensity score / linear propensity score With propensity score estimation, concern is not with the parameter estimates of the model, but rather with the resulting balance of the covariates (Augurzky and Schmidt, 2001). Implementing a matching method, given that measure of closeness. Methods: k:1 Nearest Neighbor
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