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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 ...
https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1467-6419.2007.00527.x
Jan 31, 2008 · To begin with, a first decision has to be made concerning the estimation of the propensity score. Following that one has to decide which matching algorithm to choose and determine the region of common support. Subsequently, the matching quality has to be assessed and treatment effects and their standard errors have to be estimated.Cited by: 5163
http://ftp.iza.org/dp1588.pdf
a first decision has to be made concerning the estimation of the propensity score. Following that one has to decide which matching algorithm to choose and determine the region of common support. Subsequently, the matching quality has to be assessed and treatment effects and their standard errors have to be estimated.
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://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://arxiv.org/pdf/1201.6385
Propensity score matching is a tool for causal inference in non-randomized studies that ... calipers, region of common support, matching with and without replacement, and matching one to many units. Detailed balance statistics and graphs are produced by the program. Keywords: propensity score, SPSS, custom dialog .Cited by: 149
https://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.statalist.org/forums/forum/general-stata-discussion/general/1378005-propensity-score-matching
May 31, 2017 · When doing nearest-neighbor propensity score matching, it is nothing special that your sample size is reduced because one does typically not need all control observations but only those serving as a good match to the treated observations (at least if …
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
https://en.wikipedia.org/wiki/Propensity_score
Propensity score. A propensity score is the probability of a unit (e.g., person, classroom, school) being assigned to a particular treatment given a set of observed covariates. Propensity scores are used to reduce selection bias by equating groups based on these covariates.
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