Stata 15 help for teffects aipw

[TE] teffects aipw -- Augmented inverse-probability weighting

Syntax

teffects aipw (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 multivalues treatments, only logit is available and multinomial logit is used.

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

options Description ------------------------------------------------------------------------- Model nls estimate conditional means by nonlinear least squares wnls estimate conditional means by weighted nonlinear least squares

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

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 and iweights 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> Augmented inverse-probability weighting

Statistics > Treatment effects > Binary outcomes> Augmented inverse-probability weighting

Statistics > Treatment effects > Count outcomes> Augmented inverse-probability weighting

Statistics > Treatment effects > Fractional outcomes> Augmented inverse-probability weighting

Statistics > Treatment effects > Nonnegative outcomes> Augmented inverse-probability weighting

Description

teffects aipw estimates the average treatment effect and the potential-outcome means from observational data by augmented inverse-probability weighting (AIPW). AIPW estimators combine aspects of regression-adjustment and inverse-probability-weighted methods. AIPW estimators have the double-robust property. teffects aipw 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.

nls specifies that the parameters of the outcome model be estimated by nonlinear least squares instead of the default maximum likelihood.

wnls specifies that the parameters of the outcome model be estimated by weighted nonlinear least squares instead of the default maximum likelihood. The weights make the estimator of the effect parameters more robust to a misspecified outcome model.

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

stat is one of two statistics: ate or pomeans. ate is the default.

ate specifies that the average treatment effect 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.

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

coeflegend; see [R] estimation options.

Examples

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

Estimate the average treatment effect of mbsmoke on bweight, using a probit model to predict treatment status . teffects aipw (bweight prenatal1 mmarried mage fbaby) (mbsmoke mmarried c.mage##c.mage fbaby medu, probit)

Use pomeans and aequations to obtain estimates of both potential-outcome means and view all the fitted equations underlying our estimates . teffects aipw (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 aipw (bweight prenatal1 mmarried fbaby) (mbsmoke mmarried c.mage##c.mage fbaby medu, hetprobit(c.mage)), aequations

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

Use WNLS to fit the outcome model shown above . teffects aipw (bweight prenatal1 mmarried mage fbaby) (mbsmoke mmarried c.mage##c.mage fbaby medu, probit), wnls

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

Video example

Treatment effects: Augmented inverse-probability weighting

Stored results

teffects aipw 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) aipw e(tmodel) logit, probit, or hetprobit e(omodel) linear, logit, probit, hetprobit, poisson, flogit, fprobit, or fhetprobit e(stat) statistic estimated, ate 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(cme) ml, nls, or wnls 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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