What is psmatch2 Stata?
Description. psmatch2 implements full Mahalanobis matching and a variety of propensity score matching methods to adjust for pre-treatment observable differences between a group of treated and a group of untreated. Treatment status is identified by depvar==1 for the treated and depvar==0 for the untreated observations.
How do you match propensity?
The basic steps to propensity score matching are:
- Collect and prepare the data.
- Estimate the propensity scores.
- Match the participants using the estimated scores.
- Evaluate the covariates for an even spread across groups.
What is Pstest Stata?
pstest calculates and optionally graphs several measures of the balancing of the variables in varlist between two groups (if varlist is not specified, pstest will look for the variables that were specified in the latest call of psmatch2 or of pstest).
What is kernel matching?
Kernel matching (KM) and local linear matching (LLM) are non-parametric matching estimators that use weighted averages of all individuals in the control group to construct the 10 Page 14 counterfactual outcome.
How does propensity score matching work?
Propensity score matching (PSM) is a quasi-experimental method in which the researcher uses statistical techniques to construct an artificial control group by matching each treated unit with a non-treated unit of similar characteristics. Using these matches, the researcher can estimate the impact of an intervention.
How do you calculate propensity?
The propensity score is defined as the probability of being treated conditional on individual’s covariate values: e(x) = pr(A* = 1|X* = x).
What is ATT Stata?
A review of propensity score in Stata Page 12. Average treatment effect among treated (ATT) ID.
What is ATT average treatment effect on the treated?
The average treatment effect (ATE) is a measure used to compare treatments (or interventions) in randomized experiments, evaluation of policy interventions, and medical trials. The ATE measures the difference in mean (average) outcomes between units assigned to the treatment and units assigned to the control.
How to bootstrap standard error in psmatch2?
psmatch2stores the estimate of the treatment effect on the treated in r(att), this allows bootstrapping of the standard error of the estimate (although it is unclear whether the bootstrap is valid in this context). This can be done as follows: . bootstrap r(att) : psmatch2 training age gender, out(wage)
How to run psmatch2 for multiple outcomes with different missing values?
If you have multiple outcomes with widely differing missing values you may wish to run psmatch2 separately for each of the outcomes. atewith this option the average treatment effect (ate) and average treatment effect on the untreated (atu) are reported in addition to the average treatment effect on the treated (att).
Is it possible to bootstrap in Stata?
See the documentation of bootstrapfor more details about bootstrapping in Stata. If you want to be able to replicate your results you should set seedbefore calling psmatch2. The propensity score – the conditional treatment probability – is either directly provided by the user or estimated by the program on the indepvars.
How is Mahalanobis matching used in psmatch2?
psmatch2implements full Mahalanobis matching and a variety of propensity score matching methods to adjust for pre-treatment observable differences between a group of treated and a group of untreated. Treatment status is identified by depvar==1 for the treated and depvar==0 for the untreated observations.