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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.
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://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).
https://www.stata.com/statalist/archive/2014-02/msg00472.html
Feb 11, 2014 · So, it is unexpected that the algorithm ends in only one block. One possible answer for your so short common support region is that one of your groups has a much small interval of propensity scores, that is, there is only small values of the probability to belong in the reference group, say black.
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: 5122
https://en.wikipedia.org/wiki/Propensity_score_matching
In the statistical analysis of observational data, propensity score matching (PSM) is a statistical matching technique that attempts to estimate the effect of a treatment, policy, or other intervention by accounting for the covariates that predict receiving the treatment. PSM attempts to reduce the bias due to confounding variables that could be found in an estimate of the treatment effect ...
https://www.statalist.org/forums/forum/general-stata-discussion/general/1378005-propensity-score-matching
May 31, 2017 · I'm doing a propensity score matching using the psmatch2 command in STATA. My cohort consist of 17,435 patient of whom 8,474 (49%) have gotten treatment and 8,961 (51%) have not. After using the psmatch2 command and nearest neighbor matching (caliper 0.2) I end up with a cohort consisting of only 4,584 patients. So only 26% of my total cohort.
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. …Cited by: 174
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://www.ncbi.nlm.nih.gov/pmc/articles/PMC2943670/
Feb 01, 2010 · When using those methods it may be beneficial to explicitly restrict the analysis to those individuals in the region of common support (as in Heckman et al., 1997; Dehejia and Wahba, 1999). Most analyses define common support using the propensity score, discarding individuals with propensity score values outside the range of the other group.
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