![]() The presence of a treatment effect jointly across several outcomes. microsynth can also apply an omnibus test that examines Produced as needed for each of the three methods of statistical inference Hypothesis test that assesses whether the effect is zero. For each outcome variable, the results list the estimated treatmentĮffect, as well as confidence intervals of the effect and p-values of a The software provides the user the option to output overall findings in an Excelįile. Infeasible), it is recommended that the jackknife not be used for inference. If treatment and synthetic controlĪre not easily matched based upon the model outlined in varĪnd match.out (i.e., an exact solution is infeasible or nearly Group, and if permutation methods are to be used, one set of weights isĬalculated for each permutation group. If a jackknife is toīe used, one set of weights is calculated for each jackknife replication On the point estimator via Taylor series linearization. The main weights can also be used to perform inferences Weights is calculated that is used to determine a point estimate of the Statistical inference using Taylor series linearization, a jackknife and Synthetic control subject to the given constraints), a less restrictive That the model specified by var and match.out is notįeasible (i.e., weights do not exist that exactly match treatment and LowRankQP() from the package of the same name is used. , the function calibrate() from the survey Treatment is to be matched to synthetic control as closely as possible. are similar but instead specify variables across which (and which pre-intervention time points of the outcomes) treatment is to beĮxactly matched to synthetic control. Using the respective inputs varĪnd match.out, the user specifies across which covariates and outcomes Variables are categorized as outcomes (which are time-variant) and covariates Is not believed to have an instantaneous effect, end.pre should indicate The inputĮnd.pre (which gives the last pre-intervention time period) is used toĭelineate between pre- and post-intervention. Is assessed across the post-intervention (or evaluation) period. Treatment is matched to synthetic controlĪcross the pre-intervention period, and the effect of the intervention The time range over which data are observed is segmented into pre- and ![]() ![]() synthetic control for pertinent outcomes may be performed using the microsynth works in two primary steps: 1) calculation of Synthetic control group and the treatment group across a set second set of That exactly match a treatment group to a synthetic control group acrossĪ number of variables while also minimizing the discrepancy between the Microsynth develops a synthetic control group by searching for weights Vignette('microsynth', package = 'microsynth'). Hainmueller (2010, 2011, 2014)) that is designed for data at a more granular Of Synth (see Abadie and Gardeazabal (2003) and Abadie, Diamond, However, it may also be used to calculate propensity score-type weights inĬross-sectional data. In assessment of the effect of an intervention using longitudinal data. Implements the synthetic control method for micro-level data as outlined in Description Usage Arguments Details Value References Examples
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