Stata 15 help for teffects ipw

[TE] teffects ipw -- Inverse-probability weighting

Syntax

teffects ipw (ovar) (tvar tmvarlist [, tmodel noconstant]) [if] [in] [weight] [, stat options]

ovar is a binary, count, continuous, fractional, or nonnegative outcome of interest.

tvar must contain integer values representing the treatment levels.

tmvarlist specifies the variables that predict treatment assignment in the treatment model.

tmodel Description ------------------------------------------------------------------------- Model logit logistic treatment model; the default probit probit treatment model hetprobit(varlist) heteroskedastic probit treatment model ------------------------------------------------------------------------- tmodel specifies the model for the treatment variable. For multivalued treatments, only logit is available and multinomial logit is used.

stat Description ------------------------------------------------------------------------- Stat ate estimate average treatment effect in population; the default atet estimate average treatment effect on the treated pomeans estimate potential-outcome means -------------------------------------------------------------------------

options Description ------------------------------------------------------------------------- SE/Robust vce(vcetype) vcetype may be robust, cluster clustvar, bootstrap, or jackknife

Reporting level(#) set confidence level; default is level(95) aequations display auxiliary-equation results display_options control columns and column formats, row spacing, line width, display of omitted variables and base and empty cells, and factor-variable labeling

Maximization maximize_options control the maximization process; seldom used

Advanced pstolerance(#) set tolerance for overlap assumption osample(newvar) newvar identifies observations that violate the overlap assumption control(# | label) specify the level of tvar that is the control tlevel(# | label) specify the level of tvar that is the treatment

coeflegend display legend instead of statistics -------------------------------------------------------------------------

tmvarlist may contain factor variables; see fvvarlists. bootstrap, by, jackknife, and statsby are allowed; see prefix. Weights are not allowed with the bootstrap prefix. fweights, iweights, and pweights are allowed; see weight. coeflegend does not appear in the dialog box. See [TE] teffects postestimation for features available after estimation.

Menu

Statistics > Treatment effects > Continuous outcomes > Inverse-probability weighting (IPW)

Statistics > Treatment effects > Binary outcomes > Inverse-probability weighting (IPW)

Statistics > Treatment effects > Count outcomes > Inverse-probability weighting (IPW)

Statistics > Treatment effects > Fractional outcomes > Inverse-probability weighting (IPW)

Statistics > Treatment effects > Nonnegative outcomes > Inverse-probability weighting (IPW)

Description

teffects ipw estimates the average treatment effect, the average treatment effect on the treated, and the potential-outcome means from observational data by inverse-probability weighting (IPW). IPW estimators use estimated probability weights to correct for the missing data on the potential outcomes. teffects ipw accepts a continuous, binary, count, fractional, or nonnegative outcome and allows a multivalued treatment.

See [TE] teffects intro or [TE] teffects intro advanced for more information about estimating treatment effects from observational data.

Options

+-------+ ----+ Model +------------------------------------------------------------

noconstant; see [R] estimation options.

+------+ ----+ Stat +-------------------------------------------------------------

stat is one of three statistics: ate, atet, or pomeans. ate is the default.

ate specifies that the average treatment effect be estimated.

atet specifies that the average treatment effect on the treated be estimated.

pomeans specifies that the potential-outcome means for each treatment level be estimated.

+-----------+ ----+ SE/Robust +--------------------------------------------------------

vce(vcetype) specifies the type of standard error reported, which includes types that are robust to some kinds of misspecification (robust), that allow for intragroup correlation (cluster clustvar), and that use bootstrap or jackknife methods (bootstrap, jackknife); see [R] vce_option.

+-----------+ ----+ Reporting +--------------------------------------------------------

level(#); see [R] estimation options.

aequations specifies that the results for the outcome-model or the treatment-model parameters be displayed. By default, the results for these auxiliary parameters are not displayed.

display_options: noci, nopvalues, noomitted, vsquish, noemptycells, baselevels, allbaselevels, nofvlabel, fvwrap(#), fvwrapon(style), cformat(%fmt), pformat(%fmt), sformat(%fmt), and nolstretch; see [R] estimation options.

+--------------+ ----+ Maximization +-----------------------------------------------------

maximize_options: iterate(#), [no]log, and from(init_specs); see [R] maximize. These options are seldom used.

init_specs is one of

matname [, skip copy]

# [, # ...], copy

+----------+ ----+ Advanced +---------------------------------------------------------

pstolerance(#) specifies the tolerance used to check the overlap assumption. The default value is pstolerance(1e-5). teffects will exit with an error if an observation has an estimated propensity score smaller than that specified by pstolerance().

osample(newvar) specifies that indicator variable newvar be created to identify observations that violate the overlap assumption.

control(# | label) specifies the level of tvar that is the control. The default is the first treatment level. You may specify the numeric level # (a nonnegative integer) or the label associated with the numeric level. control() may not be specified with statistic pomeans. control() and tlevel() may not specify the same treatment level.

tlevel(# | label) specifies the level of tvar that is the treatment for the statistic atet. The default is the second treatment level. You may specify the numeric level # (a nonnegative integer) or the label associated with the numeric level. tlevel() may only be specified with statistic atet. tlevel() and control() may not specify the same treatment level.

The following option is available with teffects ipw but is not shown in the dialog box:

coeflegend; [R] estimation options.

Examples

Setup . webuse cattaneo2

Estimate the average treatment effect of smoking on birthweight, using a probit model to predict treatment status . teffects ipw (bweight) (mbsmoke mmarried c.mage##c.mage fbaby medu, probit)

Estimate the average treatment effect on the treated . teffects ipw (bweight) (mbsmoke mmarried c.mage##c.mage fbaby medu, probit), atet

Estimate the average treatment effect as a percentage . teffects ipw (bweight) (mbsmoke mmarried c.mage##c.mage fbaby medu, probit), coeflegend

Video example

Treatment effects: Inverse-probability weighting

Stored results

teffects ipw stores the following in e():

Scalars e(N) number of observations e(nj) number of observations for treatment level j e(N_clust) number of clusters e(k_eq) number of equations in e(b) e(k_levels) number of levels in treatment variable e(treated) level of treatment variable defined as treated e(control) level of treatment variable defined as control e(converged) 1 if converged, 0 otherwise

Macros e(cmd) teffects e(cmdline) command as typed e(depvar) name of outcome variable e(tvar) name of treatment variable e(subcmd) ipw e(tmodel) logit, probit, or hetprobit e(stat) statistic estimated, ate, atet, or pomeans e(wtype) weight type e(wexp) weight expression e(title) title in estimation output e(clustvar) name of cluster variable e(tlevels) levels of treatment variable e(vce) vcetype specified in vce() e(vcetype) title used to label Std. Err. e(properties) b V e(estat_cmd) program used to implement estat e(predict) program used to implement predict e(marginsnotok) predictions disallowed by margins e(asbalanced) factor variables fvset as asbalanced e(asobserved) factor variables fvset as asobserved

Matrices e(b) coefficient vector e(V) variance-covariance matrix of the estimators

Functions e(sample) marks estimation sample


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