Stata 15 help for tebalance density

[TE] tebalance density -- Covariate balance density

Syntax

Density plots for the propensity score

tebalance density [, options]

Density plots for a covariate

tebalance density varname [, options]

options Description ------------------------------------------------------------------------- Main kernel(kernel) specify the kernel function; default is kernel(epanechnikov) bwidth(*#) rescale default bandwidth line#opts(line_options) twoway line options for density line number # twoway_options any options other than by() documented in [G-3] twoway_options byopts(byopts) how subgraphs are combined, labeled, etc. -------------------------------------------------------------------------

kernel Description ------------------------------------------------------------------------- triangle triangle kernel function; the default epanechnikov Epanechnikov kernel function epan2 alternative Epanechnikov kernel function biweight biweight kernel function cosine cosine trace kernel function gaussian Gaussian kernel function parzen Parzen kernel function rectangle rectangle kernel function -------------------------------------------------------------------------

Menu

Statistics > Treatment effects > Balance > Graphs

Description

tebalance density produces kernel density plots that are used to check for covariate balance after estimation by a teffects inverse-probability-weighted estimator, a teffects matching estimator, or an stteffects inverse-probability-weighted estimator.

Options

+------+ ----+ Main +-------------------------------------------------------------

kernel(kernel) specifies the kernel function for use in calculating the kernel density estimates. The default kernel is the kernel(epanechnikov).

bwidth(*#) specifies the factor by which the default bandwidths are to be rescaled. A bandwidth is the half-width of the kernel, the width of the density window around each point. Each kernel density plot has its own bandwidth, and by default, each kernel density plot uses its own optimal bandwidth; see [R] kdensity. bwidth() rescales each plot's optimal bandwidth by the specified amount.

line#opts(line_options) specifies the line pattern, width, color, and overall style of density line number #. The line numbers are in the same order as the treatment levels specified in e(tlevels).

twoway_options are any of the options documented in [G-3] twoway_options, excluding by(). These include options for titling the graph (see [G-3] title_options) and for saving the graph to disk (see [G-3] saving_option). tebalance density uses by() to differentiate between raw and weighted or matched samples, and some twoway_options will be repeated for by graph and might be better specified as byopts().

byopts(by_option) is as documented in [G-3] by_options. byopts() affects how the subgraphs are combined, labeled, etc. byopts() generally affects the entire graph, and some by_option may be better specified as twoway_options; see [G-3] twoway_options.

Example

Setup . webuse cattaneo2

Estimate the effect of a mother's smoking behavior (mbsmoke) on the birthweight of her child (bweight), controlling for marital status (mmarried), the mother's age (mage), whether the mother had a prenatal doctor's visit in the baby's first trimester (prenatal1), and whether this baby is the mother's first child (fbaby) . teffects psmatch (bweight) (mbsmoke mmarried mage prenatal1 fbaby), generate(matchv)

Look at the default density plots . tebalance density mage

Stored results

After teffects or stteffects fits a binary treatment, tebalance density stores the following in r():

Scalars r(bwc_adj) bandwidth for control in weighted or matched-adjusted sample r(Nc_adj) observations on control in weighted or matched-adjusted sample r(bwt_adj) bandwidth for treated in weighted or matched-adjusted sample r(Nt_adj) observations on treated in weighted or matched-adjusted sample r(bwc_raw) bandwidth for control in raw sample r(Nc_raw) observations on control in raw sample r(bwt_raw) bandwidth for treated in raw sample r(Nt_raw) observations on treated in raw sample

Macros r(kernel) name of kernel

After teffects or stteffects fits a multivalued treatment, tebalance density stores the following in r():

Scalars r(bw#_adj) bandwidth for treatment level # in weighted or matched-adjusted sample r(N#_adj) observations on treatment level # in weighted or matched-adjusted sample r(bw#_raw) bandwidth for treatment level # in raw sample r(N#_raw) observations on treatment level # in raw sample

Macros r(kernel) name of kernel


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