help sensimatch

Title

sensimatch- Data-driven sensitivity analysis for Matching estimator

Syntax

sensimatch
outcome
treatment
[varlist]
[if] [in]
[weight],
mod(modeltype)
sims(number)
[fac(varlist_f)
seed(number)
save_sens(filename)
vce(vcetype)
gr_dep_var(text)
gr1_save(filename)
gr1_title(text)
gr1_xtitle(text)
gr1_ysize(size)
gr1_xsize(size)
gr2_save(filename)
gr2_title(text)
gr2_xtitle(text)
gr2_ysize(size)
gr2_xsize(size)]

fweights, pweights, iweights are allowed only for model "reg";
see weight.

Description

sensimatch provides a sensitivity test for checking the robustness of the selection-on-observables assumption
in treatment effect observational studies, both within a regression adjustment and a propensity-score matching approach.
Rooted in the machine learning literature, this sensitivity analysis is
based on a "leave-one-covariate-out" (LOCO) approach. This method recalls a bootstrap over different subsets of covariates, and simulates
various estimation scenarios to be compared with the baseline results obtained by the analyst.
The main output of sensimatch is graphical, thus providing the user with an easy-to-interpret robustness check
of his/her study results.

Options

General options

mod(modeltype) specifies the model
to be estimated, where modeltype must be one of the following
models: "reg" or "match". It is always required to specify one model.

sims(number) specifes the number of model simulations for each set of included covariates.

fac(varlist_f) specifies that factor variables have to be included
among the regressors. It is optional in each model.

seed(number) specifies the random generation seed for each simulation.

save_sens(filename) allows to save the variables used to generate the sensitivity graph.

vce(vcetype): allows to choose vcetype as either robust, or cluster clustvar. This option works only for model "reg".

gr_dep_var(text) allows to customize the name of the dependent variable appearing in the sensitivity graph.

Graph options

Graph 1 - ATET

gr1_save(filename) allows to save the sensitivity graph for ATET by a user specifed filename.

gr1_title(text) allows to customize the overall title of the sensitivity graph for ATET.

gr1_xtitle(text) allows to customize the x-axis title of the sensitivity graph for ATET.

gr1_ysize(size) allows to customize the size of the y-axis title of the sensitivity graph for ATET.

gr1_xsize(size) allows to customize the size of the x-axis title of the sensitivity graph for ATET.

Graph 2 - T-Student

gr2_save(filename) allows to save the sensitivity graph for the T-Student by a user specifed filename.

gr2_title(text) allows to customize the overall title of the sensitivity graph for T-Student.

gr2_xtitle(text) allows to customize the x-axis title of the sensitivity graph for T-Student.

gr2_ysize(size) allows to customize the size of the y-axis title of the sensitivity graph for T-StudentT.

gr2_xsize(size) allows to customize the size of the x-axis title of the sensitivity graph for T-Student.

modeltype_options

Model
reg Regression estimated by ordinary least squares (OLS)

match Propensity-score matching

Remarks

Please, before running this program, remember to have the most recent up-to-date version installed.

Examples

*----------------------------------------------------------------------------------------------------------
. webuse nlsw88 , clear
. global y "wage"
. global w "union"
. global xvars "age race married never_married grade south smsa c_city collgrad hours ttl_exp tenure"
. global factors "industry occupation"
*----------------------------------------------------------------------------------------------------------
. sensimatch $y $w $xvars if c_city==1 , mod(match) sims(5) vce(robust) ///
fac($factors) save_sens(data_sens) seed(1010) ///
gr_dep_var("Wage") ///
gr1_title("") ///
gr1_xtitle(Number of included covariates) ///
gr1_ytitle(ATET) ///
gr1_xsize(small) ///
gr1_ysize(small) ///
gr1_save(mygraph1) ///
gr2_title("") ///
gr2_xtitle(Number of included covariates) ///
gr2_ytitle(T-Student) ///
gr2_xsize(small) ///
gr2_ysize(vsmall) ///
gr2_save(mygraph2)
*----------------------------------------------------------------------------------------------------------

Reference

Cerulli, G. 2015. Econometric Evaluation of Socio-Economic Programs: Theory and Applications.
Springer, Berlin.

Nannicini, T. 2007. Simulation-based sensitivity analysis for matching estimators.
The Stata Journal, 7, Number 3, pp. 334-350.

Rosenbaum, P. R. 1987. Sensitivity analysis to certain permutation inferences in matched observational studies.
Journal of the Royal Statistical Society, Series B, 45, pp. 212-218.

Rosenbaum, P. R., and Rubin, D. B. 1983. Assessing sensitivity to an unobserved binary covariate in an observational
study with binary outcome. Journal of the Royal Statistical Society, Series B, 45, pp. 212-218.

Author

Giovanni Cerulli

IRCrES-CNR

Research Institute for Sustainable Economic Growth, National Research Council of Italy

E-mail: giovanni.cerulli@ircres.cnr.it

Also see

Online: sensatt, rbounds