help hte
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Title

hte -- Heterogeneous Treatment Effect Analysis

Syntax

hte depvar1 [depvar2 ... =] treatvar indepvars [if] [in] [weight] [, options ]

hte graph [, level(#) graph_options ]

options Description ------------------------------------------------------------------------- Main alpha(#) set significance level for balancing tests pscore_options options as described in help pscore (except level()) join(list) merge specified strata autojoin[(#)] merge small strata at low end or high end by(groupvar) repeat analyses for groups defined by groupvar separate construct propensity score strata separately for each by-group controls(clist) control variables for within-strata models estcom(command) set estimation command for within strata models; default is regress estopts(options) options to be applied to within-strata models noisily display output from pscore and individual models level(#) set confidence level; default is level(95) listwise use listwise deletion to handle missing values casewise synonym for listwise

Graph nograph suppress graph outcomes(numlist) display results for specified outcomes marker_options change look of markers lineopts(options) change look of fitted lines ciopts(options) change look of confidence intervals noci suppress confidence intervals addplot(plot) add other plots to the generated graph twoway_options any options other than by() documented in [G] twoway_options ------------------------------------------------------------------------- fweights, iweights, and pweights are allowed; see help weight.

Description

hte applies pscore (Becker and Ichino 2002) to construct balanced propensity score strata and, within each stratum, estimates the average treatment effect. hte then tests for linear trend in treatment effects using variance-weighted least squares. The stratum-specific treatment effects and the estimated linear trend are displayed in a twoway graph.

See Brand and Xie (2010) for an application of this procedure.

hte specified without arguments redisplays the results from a previous hte call (without graph).

hte graph redraws the graph based on the results from a previous hte call.

Dependencies

hte requires pscore by Becker and Ichino (2002) to be installed on the system. Type

. net sj 5-3 st0026_2

followed by

. net install st0026_2

to install the program.

Options

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

alpha(#) sets the significance level for pscore's tests of the balancing property. The default is alpha(0.01).

pscore_options are any options as described in help pscore, except level().

join(list) causes the specified strata to be merged together for the treatment effect analysis. The syntax for list is

numlist [, numlist ...]

where numlist is a list of consecutive integers that identify the strata to be merged. For example, type join(1 2) to merge the first and second stratum. Multiple (disjunctive) numlists may be specified, separated by a comma, in which case multiple merges are applied. After merging, the strata will be renumbered.

autojoin[(#)] causes small strata at the low and high end of the propensity score to be merged with subsequent or precedent strata, respectively, so that the number of observations is at least # for both the treated and the untreated (# defaults to 10). Only one of join() and autojoin() may be specified.

by(groupvar) specifies that the analysis be repeated for each group defined by the values of groupvar. The results are plotted in a single graph for all by-groups. Common propensity score strata are used for all groups unless the separate option is specified.

separate causes the construction of propensity score strata to be repeated for each by-group. The default is to use common strata, that is, to construct the strata once, based on the whole sample including all groups. separate has an effect only if by() is specified.

controls(clist) specifies control variables to be included in the models used to estimate the within-strata treatment effects. clist may be a standard varlist, in which case the specified variables are included in each within-strata model. Alternatively, use the following syntax to specify strata-specific sets:

[varlist] [numlist1: varlist1] [numlist2: varlist2] [...]

varlist applies to all strata, varlist1 applies to the strata specified in numlist1, etc.

estcom(command) sets the command used to estimate the within strata treatment effects. The default is regress.

estopts(options) are options to be applied to the models used to estimate the within-strata treatment effects. The options are as described in help regress (or as in help command where, command is the command specified via the estcom() option).

noisily displays the output from pscore and the treatment effect models. pscore's detail option implies noisily.

level(#) sets the confidence level, as a percentage, for confidence intervals. The default is level(95) or as set by set level.

listwise handles missing values through listwise deletion, meaning that an observation is excluded from all computations if any of the specified variables is missing for that observation. By default, hte uses all available observations to compute the propensity strata without regard to whether values for the outcome variables (the depvars) or the variables specified in controls() are missing.

casewise is a synonym for listwise.

+-------+ ----+ Graph +------------------------------------------------------------

nograph suppresses the graph.

outcomes(numlist) causes results to be plotted for the specified outcomes only. Use this option to select results in case of multiple outcome variables or by()-groups. Use numbers 1, 2, 3, etc. to refer to the different outcomes. By-groups are ordered within variables if both multiple outcome variables and the by() option are specified. That is, number 1 refers to depvar1 and the first by-group, number 2 refers to depvar1 and the second by-group, etc.

marker_options affect the rendition of the plotted markers, including their shape, size, color, and outline; see [G] marker_options. In case of multiple outcomes you can usually specify lists of elements to be applied to the different outcomes. For example, type msymbol(D T) to use diamonds for the first outcome and triangles for the second outcome.

lineopts(cline_options) affects the rendition of the plotted lines; see [G] cline_options. In case of multiple outcomes you can usually specify lists of elements to be applied to the different outcomes. For example, type lineopts(lcolor(blue red)) to use blue line color for the first outcome and red line color for the second outcome.

ciopts(options) affects the rendition of the capped confidence spikes for the within strata treatment effects; see [G] graph twoway rcap.

noci suppresses the confidence intervals for the within strata treatment effects. Confidence intervals are only displayed on plots containing a single outcome.

addplot(plot) provides a way to add other plots to the generated graph; see [G] addplot_option.

twoway_options are any options other than by() documented in [G] twoway_options.

Examples

Treatment effect of college on wages:

. sysuse nlsw88 . generate sq_exp = ttl_exp^2 . hte wage collgrad ttl_exp sq_exp tenure south smsa

Add control variables to within strata treatment effect estimation:

. hte wage collgrad ttl_exp sq_exp tenure south smsa, control(ttl_exp s > q_exp)

Separate results by union:

. hte wage collgrad ttl_exp sq_exp tenure south smsa, by(union)

Redraw graph for second group (union=1):

. hte graph, outcome(2)

Saved results

hte saves the following in e():

Scalars e(N) number of observations e(neq) number of equations (outcomes)

Macros e(cmd) hte e(estcom) estimation command as specified by estcom() e(depvar) name(s) of dependent variable(s) e(treatvar) name of treatment variable e(indepvars) name(s) of independent variables e(controls) expanded controls option e(trend) note about linear fit e(byvar) name of by() variable e(depvar#) outcome #: name of dependent variable e(by#) outcome #: by-group e(trend#) outcome #: note about linear fit e(wtype) weight type e(wexp) weight expression e(properties) b

Matrices e(b) results vector e(se) standard errors e(obs) number of observations per stratum e(block) strata ranks e(lfit) linear fit of treatment effect by strata rank

Functions e(sample) estimation sample

References

Becker, Sascha O., and Andrea Ichino. 2002. Estimation of average treatment effects based on propensity scores. The Stata Journal 2:358–377.

Brand, Jennie E. and Yu Xie. 2010. Who Benefits Most from College? Evidence for Negative Selection in Heterogeneous Economic Returns to Higher Education. American Sociological Review 75(2).

Authors

Ben Jann, ETH Zurich, jannb@ethz.ch

Jennie E. Brand, University of Calofornia - Los Angeles, brand@soc.ucla.edu

Yu Xie, University of Michigan, yuxie@isr.umich.edu

Thanks for citing this software as follows:

Jann, B., J. E. Brand, Y. Xie. 2010. Stata module to perform heterogeneous treatment effect analysis. Available from http://ideas.repec.org/c/boc/bocode/s457129.html.

Also see