mediation (version 4.2.2)

mediate.pd: Estimating Average Causal Mediation Effects under the Parallel Design

Description

'mediate.pd' estimates the average causal mediation effects for the parallel design. If a treatment-mediator interaction is allowed then the nonparametric sharp bounds are calculated. If a treatment-mediator interaction is not allowed then the estimates of the (point-identified) effects are computed along with bootstrapped confidence intervals.

Usage

mediate.pd(outcome, mediator, treat, manipulated, data,
           NINT = TRUE, sims = 1000, conf.level = 0.95)

Arguments

outcome

name of the outcome variable in 'data'.

mediator

name of the mediator in 'data'. The variable must be binary (factor or numeric 0/1).

treat

name of the treatment variable in 'data'. Must be binary (factor or numeric 0/1).

manipulated

name of the binary design indicator in 'data', indicating whether observation received mediator manipulation.

data

a data frame containing all the above variables.

NINT

whether the no interaction assumption is made.

sims

number of bootstrap simulations. Only relevant when 'NINT' is TRUE.

conf.level

level of the returned two-sided confidence intervals. Only relevant when 'NINT' is TRUE.

Value

• mediate.pd returns an object of class "mediate.design", a list that contains the components listed below.

  The function summary (i.e., summary.mediate.design) can be used to obtain a table of the results.

• d0, d1
  point estimates or bounds for the average causal mediation effects under the control and treatment conditions, respectively.
• d0.ci, d1.ci
  confidence intervals for the effects based on the nonparametric bootstrap. The confidence level is set at the value specified in 'conf.level'. Only exists when 'NINT' is TRUE.
• nobs
  number of observations used.
• conf.level
  confidence level used. Only exists when 'NINT' is TRUE.
• sims
  number of bootstrap simulations used for confidence interval calculation. Only exists when 'NINT' is TRUE.
• design
  indicates the design. "PD.NINT" if no interaction assumed; "PD" if interaction allowed.

Details

This function calculates average causal mediation effects (ACME) for the parallel design. The design consists of two randomly separated experimental arms, indicated by 'manipulated'. In one the treatment is randomized and the mediator and outcome variables are measured. In the second arm, the treatment is randomized, the mediator is perfectly manipulated and the outcome variable is measured.

Under the parallel design, the ACME is identified when it is assumed that there is no interaction between the treatment and mediator. Without the assumption the nonparametric sharp bounds can be computed. See Imai, Tingley and Yamamoto (2012) for details.

References

Imai, K., Tingley, D. and Yamamoto, T. (2012) Experimental Designs for Identifying Causal Mechanisms. Journal of the Royal Statistical Society, Series A (Statistics in Society)"

Imai, K., Keele, L., Tingley, D. and Yamamoto, T. (2011). Unpacking the Black Box of Causality: Learning about Causal Mechanisms from Experimental and Observational Studies, American Political Science Review, Vol. 105, No. 4 (November), pp. 765-789.

Imai, K., Keele, L. and Yamamoto, T. (2010) Identification, Inference, and Sensitivity Analysis for Causal Mediation Effects, Statistical Science, Vol. 25, No. 1 (February), pp. 51-71.

Imai, K., Keele, L., Tingley, D. and Yamamoto, T. (2009) Causal Mediation Analysis Using R" in Advances in Social Science Research Using R, ed. H. D. Vinod New York: Springer.

See Also

mediate, summary.mediate.design

Examples

data(boundsdata)

bound2 <- mediate.pd("out", "med", "ttt", "manip", boundsdata, 
                  NINT = TRUE, sims = 100, conf.level=.95)
summary(bound2)

bound2.1 <- mediate.pd("out", "med", "ttt", "manip", boundsdata, NINT = FALSE)
summary(bound2.1)