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help for medeff
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 Function to estimate causal mediation effects.

        medeff (equation 1) (equation 2) [if] [in] [[weight]] , [sims(integer)
                           seed(integer) vce(vcetype) Level(#)
                           interact)(varname) ] mediate(varname) treat(varname)


Description

medeff is the workhorse function for estimating mediation effects for a variety
of data types. For a continuous mediator variable and a continuous outcome
variable, the results will be identical to the usual Baron and Kenny method.
The function can, however, accomodate other data types including binary
outcomes and mediators and calculate the correct estimates.

SE/Robust

vce(vcetype)vcetype may be robust, cluster clustvar, bootstrap, or jackknife.

Options

sims(integer) specifies the number of simulations to run for the quasi-Bayesian
    approximation of parameter uncertainty. The default value is 1000. Higher
    values will increase the computational time.

seed(integer) sets the random number seed for precise replicability though with
    sufficient sims results will be very similar. The default value is the
    random seed Stata draws when starting a session.

 
vcetype(vcetype) allows users to specify how the standard errors will be
    calculated.

 
Level(#) sets the confidence interval; default is level(95).

 
interact(varname) allows for an interaction between the treatment and mediating
    variable. Interaction terms must be created prior to running and included
    in the model for the outcome variable.

mediate(varname) is required and specifies the mediating variable to be used in
    the analysis

treat(varname integer integer) is also required and specifies the treatment
    variable used in the analysis.  The values of the treatment variable can be
    specified. If no values are specified, values of 0 and 1 are assumed.

Examples

    . medeff (regress M T x) (regress Y T M x) , treat(T) mediate(M)
    sims(1000) seed(1)
    . medeff (probit M T x) (regress Y T M x) , treat(T) mediate(M)
    sims(1000)
    . medeff (regress M T x) (probit Y T M x) , treat(T) mediate(M)
    sims(1000)
    . medeff (regress M T x) (probit Y T M x) if x>0 , treat(T) mediate(M)
    sims(1000)

Remarks

medeff only supports OLS, probit, and logit in either stage of the analysis so
far.  Any estimation technique other than those will produce an error.  After
conducting mediation analysis users should conduct a formal sensitivity
analysis and report these results (see medsens).  Currently the mediate
function does not handle interactions between pre-treatment x variables and
either the treatment or mediator.  A requirement for causal mediation analysis
is that the same observations are used in the mediator and outcome regressions.
The function will automatically restrict samples in order to do this.

Saved results

The program stores the Average Causal Mediation Effect (ACME) in the scalars
r(delta0) and r(delta0). The direct effects are stored in r(zeta0) and r(zeta1)
and the total effect is stored in r(tau). For each of these, the low and high
bounds of the confidence interval are stored in the lo and hi scalars.

Authors 
 
        Raymond Hicks 
        Niehaus Center for Globalization and Governance, Princeton University 
  
        Dustin Tingley 
        Government Department, Harvard University
  
Email rhicks@princeton.edu or dtingley@gov.harvard.edu if you observe any probl
> ems. 
        
Please cite upon use: Hicks, Raymond and Dustin Tingley (2011) mediation: STATA
package for causal mediation analysis, as well as the below journal articles
that form the theoretical basis of the package.

Also see

Further details on the analytical formulas and on the algorithms used in these
programs can be found under http://imai.princeton.edu/projects/mechanisms.html

Bibliography and Sources

The procedures used for estimation are discussed extensively elsewhere.

    Imai, Kosuke, Luke Keele and Dustin Tingley (2010) A General Approach to
      Causal Mediation Analysis, Psychological Methods 15(4) pp. 309-334.

    Imai, Kosuke, Luke Keele and Teppei Yamamoto (2010) Identification,
      Inference, and Sensitivity Analysis for Causal Mediation Effects,
      Statistical Sciences, 25(1) pp. 51-71.

    Imai, Kosuke, Luke Keele, Dustin Tingley, and Teppei Yamamoto. (2009)
      "Causal Mediation Analysis Using R" in Advances in Social Science
      Research Using R, ed. H. D. Vinod New York: Springer. Forthcoming.

A companion package in R is available at
  http://cran.r-project.org/web/packages/mediation/.