'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.
mediate.pd(outcome, mediator, treat, manipulated, data, NINT = TRUE,
sims = 1000, conf.level = 0.95)name of the outcome variable in 'data'.
name of the mediator in 'data'. The variable must be binary (factor or numeric 0/1).
name of the treatment variable in 'data'. Must be binary (factor or numeric 0/1).
name of the binary design indicator in 'data', indicating whether observation received mediator manipulation.
a data frame containing all the above variables.
whether the no interaction assumption is made.
number of bootstrap simulations. Only relevant when 'NINT' is TRUE.
level of the returned two-sided confidence intervals. Only relevant when 'NINT' is TRUE.
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.
point estimates or bounds for the average causal mediation effects under the control and treatment conditions, respectively.
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.
number of observations used.
confidence level used. Only exists when 'NINT' is TRUE.
number of bootstrap simulations used for confidence interval calculation. Only exists when 'NINT' is TRUE.
indicates the design. "PD.NINT" if no interaction assumed; "PD" if interaction allowed.
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.
Tingley, D., Yamamoto, T., Hirose, K., Imai, K. and Keele, L. (2014). "mediation: R package for Causal Mediation Analysis", Journal of Statistical Software, Vol. 59, No. 5, pp. 1-38.
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.
# NOT RUN {
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)
# }
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