Stata 15 help for teffects ipwra

[TE] teffects ipwra -- Inverse-probability-weighted regression adjustment

Syntax

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

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

omvarlist specifies the covariates in the outcome model.

tvar must contain integer values representing the treatment levels.

tmvarlist specifies the covariates in the treatment-assignment model.

omodel Description ------------------------------------------------------------------------- Model linear linear outcome model; the default logit logistic outcome model probit probit outcome model hetprobit(varlist) heteroskedastic probit outcome model poisson exponential outcome model flogit fractional logistic outcome model fprobit fractional probit outcome model fhetprobit(varlist) fractional heteroskedastic probit outcome model ------------------------------------------------------------------------- omodel specifies the model for the outcome variable.

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 -------------------------------------------------------------------------

omvarlist and 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 > Regression adjustment with IPW

Statistics > Treatment effects > Binary outcomes > Regression adjustment with IPW

Statistics > Treatment effects > Count outcomes > Regression adjustment with IPW

Statistics > Treatment effects > Fractional outcomes > Regression adjustment with IPW

Statistics > Treatment effects > Nonnegative outcomes > Regression adjustment with IPW

Description

teffects ipwra estimates the average treatment effect, the average treatment effect on the treated, and the potential-outcome means from observational data by inverse-probability-weighted regression adjustment (IPWRA). IPWRA estimators use weighted regression coefficients to compute averages of treatment-level predicted outcomes, where the weights are the estimated inverse probabilities of treatment. The contrasts of these averages estimate the treatment effects. IPWRA estimators have the double-robust property. teffects ipwra 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 ipwra but is not shown in the dialog box:

coeflegend; see [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 ipwra (bweight prenatal1 mmarried mage fbaby) (mbsmoke mmarried c.mage##c.mage fbaby medu, probit)

--------------------------------------------------------------------------- Setup . webuse cattaneo2

Display the POMs and equations . teffects ipwra (bweight prenatal1 mmarried mage fbaby) (mbsmoke mmarried c.mage##c.mage fbaby medu, probit), pomeans aequations

Refit the above model, but use heteroskedastic probit to model the treatment variable . teffects ipwra (bweight prenatal1 mmarried fbaby c.mage) (mbsmoke mmarried c.mage##c.mage fbaby medu, hetprobit(c.mage##c.mage)), aequations

---------------------------------------------------------------------------

Video example

Treatment effects: Inverse-probability-weighted regression adjustment

Stored results

teffects ipwra 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) ipwra e(tmodel) logit, probit, or hetprobit e(omodel) linear, logit, probit, hetprobit, poisson, flogit, fprobit, or fhetprobit 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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