Description Usage Arguments Details Value Author(s) References See Also Examples
'medsens' is used to perform sensitivity analysis on the average causal
mediation effects and direct effects for violations of the sequential
ignorability assumption. The function takes output from 'mediate
' and
calculates the true average causal mediation effects and direct effects for
different values of the sensitivity parameter representing the degree of the
sequential ignorability violation.
1 2 3 
x 
an object of class 'mediate', typically an output from the

rho.by 
a numeric value between 0 and 1 indicating the increment for the sensitivity parameter, rho. 
sims 
the number of Monte Carlo draws for the calculation of confidence intervals. Only used in cases where either the mediator or outcome variable is binary. 
eps 
convergence tolerance parameter for the iterative FGLS. Only used when both the mediator and outcome models are linear. 
effect.type 
a character string indicating which effect(s) to be analyzed. Default is "indirect". 
This is the workhorse function for sensitivity analyses for average
causal mediation effects. The sensitivity analysis can be used to assess
the robustness of the findings from mediate
to the violation of
sequential ignorability, the crucial identification assumption necessary
for the estimates to be valid. The analysis proceeds by quantifying the
degree of sequential ignorability violation as the correlation between the
error terms of the mediator and outcome models, and then calculating the
true values of the average causal mediation effect for given values of this
sensitivity parameter, rho. The original findings are deemed sensitive if
the true effects are found to vary widely as function of rho.
The sensitivity analysis is only implemented for the following three model combinations: linear mediator and outcome models (both of class 'lm'), binary probit mediator (fitted via 'glm' with family "binomial" and link "probit") and linear outcome models, and linear mediator and binary probit outcome models. In addition, the binary outcome model cannot include a treatmentmediator interaction term. An error is returned if the 'mediate' object in 'x' is based on other model combinations. As of version 3.0, the sensitivity analysis can also be conducted with respect to the average direct effect by setting 'effect.type' to "direct" (or "both" if results for the average causal mediation effect are also desired).
Users should note that computation can take significant time for
medsens
. Setting 'rho.by' to a larger number significantly decreases
computational time, as does decreasing 'eps' (for the linearlinear case)
or the number of simulations 'sims' (for the binarylinear and
linearbinary cases).
medsens
returns an object of class "medsens
", a list
containing the following elements. Some of these elements are not available
depending on the 'effect.type' argument specified by the user. The output
can then be passed to the summary
(i.e.,
summary.medsens
) and plot
(i.e.,
plot.medsens
) functions to produce tabular and graphical
summaries of the results.
d0, d1 
vectors of point estimates for average causal mediation effects under the control and treatment conditions for each value of sensitivity parameter rho. 
upper.d0, lower.d0, upper.d1, lower.d1 
vectors of upper and lower confidence limits for average causal mediation effect under the control and treatment conditions for each value of rho. 
z0, z1 
vectors of point estimates for average direct effect under the control and treatment conditions for each value of sensitivity parameter rho. 
upper.z0, lower.z0, upper.z1, lower.z1 
vectors of upper and lower confidence limits for average direct effect under the control and treatment conditions for each value of rho. 
tau 
a vector of point estimates for total effect for each value of rho. Only present when the outcome model is binary. 
upper.tau, lower.tau 
vectors of upper and lower confidence limits for total effect. Only present when the outcome model is binary. 
nu 
a vector of point estimates for the proportion mediated for each value of rho. Only present when the outcome model is binary. 
upper.nu, lower.nu 
vectors of upper and lower confidence limits for the proportion mediated. Only present when the outcome model is binary. 
rho 
a numeric vector containing the values of sensitivity parameter rho used. 
rho.by 
a numeric value indicating the increment of rho used. 
sims 
a numeric value indicating the number of Monte Carlo draws used. 
err.cr.d, err.cr.z 
the values of rho with which the average causal mediation and direct effects are zero. Vectors of length two if 'INT' is 'TRUE'; numeric values otherwise. 
ind.d0, ind.d1, ind.z0, ind.z1 
vectors of 0s/1s, indicating whether the confidence intervals of d0, d1, z0 and z1 do not cover zero for each value of rho. 
R2star.prod 
a numeric vector containing the values of the products
of the two "R square stars", representing the proportions of residual
variance in the mediator and outcome explained by the hypothesized
unobserved confounder. The values correspond to those of rho. See

R2tilde.prod 
a numeric vector containing the values of the products
of the two "R square tildes", representing the proportions of total
variance in the mediator and outcome explained by the hypothesized
unobserved confounder. The values correspond to those of rho. See

R2star.d.thresh, R2star.z.thresh 
the values of the product of "R square stars" for which the average causal mediation and direct effects are zero, respectively. 
R2tilde.d.thresh, R2tilde.z.thresh 
the values of the product of "R square tildes" for which the average causal mediation and direct effects are zero, respectively. 
r.square.y, r.square.m 
the usual R square statistics for the outcome and mediator models. 
INT 
a logical value indicating whether interaction between the treatment and mediator is allowed in the original mediate object. 
conf.level 
the confidence level used. 
effect.type 
the 'effect.type' argument used. 
type 
a character string indicating the type of the mediator and outcome models used. Currently either "ct" (linear mediator and outcome models), 'bm' (binary mediator and linear outcome models) or 'bo' (linear mediator and binary outcome models). 
robustSE 
‘TRUE’ or ‘FALSE’. 
cluster 
the clusters used. 
Dustin Tingley, Harvard University, dtingley@gov.harvard.edu; Teppei Yamamoto, Massachusetts Institute of Technology, teppei@mit.edu; Jaquilyn WaddellBoie, Princeton University, jwaddell@princeton.edu; Kentaro Hirose, Princeton University, hirose@princeton.edu; Luke Keele, Penn State University, ljk20@psu.edu; Kosuke Imai, Princeton University, kimai@princeton.edu.
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. 138.
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. 765789.
Imai, K., Keele, L. and Tingley, D. (2010) A General Approach to Causal Mediation Analysis, Psychological Methods, Vol. 15, No. 4 (December), pp. 309334.
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. 5171.
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.
mediate
, summary.medsens
,
plot.medsens
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50  # Examples with JOBS II Field Experiment
# **For illustration purposes a small number of simulations are used**
data(jobs)
####################################################
# Example 1: Binary treatment
####################################################
# Fit parametric models
b < lm(job_seek ~ treat + econ_hard + sex + age, data=jobs)
c < lm(depress2 ~ treat + job_seek + econ_hard + sex + age, data=jobs)
# Pass model objects through mediate function
med.cont < mediate(b, c, treat="treat", mediator="job_seek", sims=50)
# med.cont < mediate(b, c, treat="treat", mediator="job_seek", sims=50, robustSE = T)
# jobs$cluster < rep(1:30, each = 30)[1]
# med.cont < mediate(b, c, treat="treat", mediator="job_seek", sims=50, cluster = jobs$cluster)
# Pass mediate output through medsens function
sens.cont < medsens(med.cont, rho.by=.1, eps=.01, effect.type="both")
# Use summary function to display results
summary(sens.cont)
# Plot true ACMEs and ADEs as functions of rho
par.orig < par(mfrow = c(2,2))
plot(sens.cont, main="JOBS", ylim=c(.2,.2))
# Plot true ACMEs and ADEs as functions of "R square tildes"
plot(sens.cont, sens.par="R2", r.type="total", sign.prod="positive")
par(par.orig)
####################################################
# Example 2: Categorical treatment
####################################################
## Not run:
# Purely for illustration, think of educ as a ``treatment''
b < lm(job_seek ~ educ + sex, data=jobs)
c < lm(depress2 ~ educ + job_seek + sex, data=jobs)
# compare two categories of educ  gradwk and somcol
med.cont < mediate(b, c, treat="educ", mediator="job_seek", sims=50,
control.value = "gradwk", treat.value = "somcol")
sens.cont < medsens(med.cont, rho.by=.1, eps=.01, effect.type="both")
summary(sens.cont)
## End(Not run)

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