Efficient Causal Mediation Analysis for the Natural and Interventional Effects
Authors: Nima Hejazi, Iván Díaz, and Kara Rudolph
The medoutcon R package provides facilities for efficient estimation
of path-specific (in)direct effects that measure the impact of a
treatment variable medoutcon
integrates with the sl3 R package
(Coyle et al. 2021) to leverage statistical machine learning in the
estimation procedure.
Install the most recent stable release from GitHub via
remotes:
remotes::install_github("nhejazi/medoutcon")To illustrate how medoutcon may be used to estimate stochastic
interventional (in)direct effects of the exposure (A) on the outcome
(Y) in the presence of mediator(s) (M) and a mediator-outcome
confounder (Z), consider the following example:
library(data.table)
library(tidyverse)
#> ── Attaching packages ─────────────────────────────────────────────────────── tidyverse 1.3.2 ──
#> ✔ ggplot2 3.3.6 ✔ purrr 0.3.4
#> ✔ tibble 3.1.8 ✔ dplyr 1.0.10
#> ✔ tidyr 1.2.1 ✔ stringr 1.4.1
#> ✔ readr 2.1.2 ✔ forcats 0.5.2
#> ── Conflicts ────────────────────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::between() masks data.table::between()
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::first() masks data.table::first()
#> ✖ dplyr::lag() masks stats::lag()
#> ✖ dplyr::last() masks data.table::last()
#> ✖ purrr::transpose() masks data.table::transpose()
library(medoutcon)
#> medoutcon v0.1.6: Efficient Natural and Interventional Causal Mediation Analysis
set.seed(1584)
# produces a simple data set based on ca causal model with mediation
make_example_data <- function(n_obs = 1000) {
## baseline covariates
w_1 <- rbinom(n_obs, 1, prob = 0.6)
w_2 <- rbinom(n_obs, 1, prob = 0.3)
w_3 <- rbinom(n_obs, 1, prob = pmin(0.2 + (w_1 + w_2) / 3, 1))
w <- cbind(w_1, w_2, w_3)
w_names <- paste("W", seq_len(ncol(w)), sep = "_")
## exposure
a <- as.numeric(rbinom(n_obs, 1, plogis(rowSums(w) - 2)))
## mediator-outcome confounder affected by treatment
z <- rbinom(n_obs, 1, plogis(rowMeans(-log(2) + w - a) + 0.2))
## mediator -- could be multivariate
m <- rbinom(n_obs, 1, plogis(rowSums(log(3) * w[, -3] + a - z)))
m_names <- "M"
## outcome
y <- rbinom(n_obs, 1, plogis(1 / (rowSums(w) - z + a + m)))
## construct output
dat <- as.data.table(cbind(w = w, a = a, z = z, m = m, y = y))
setnames(dat, c(w_names, "A", "Z", m_names, "Y"))
return(dat)
}
# set seed and simulate example data
example_data <- make_example_data()
w_names <- str_subset(colnames(example_data), "W")
m_names <- str_subset(colnames(example_data), "M")
# quick look at the data
head(example_data)
#> W_1 W_2 W_3 A Z M Y
#> 1: 1 0 1 0 0 0 1
#> 2: 0 1 0 0 0 1 0
#> 3: 1 1 1 1 0 1 1
#> 4: 0 1 1 0 0 1 0
#> 5: 0 0 0 0 0 1 1
#> 6: 1 0 1 1 0 1 0
# compute one-step estimate of the interventional direct effect
os_de <- medoutcon(W = example_data[, ..w_names],
A = example_data$A,
Z = example_data$Z,
M = example_data[, ..m_names],
Y = example_data$Y,
effect = "direct",
estimator = "onestep")
os_de
#> Interventional Direct Effect
#> Estimator: onestep
#> Estimate: -0.065
#> Std. Error: 0.054
#> 95% CI: [-0.17, 0.041]
# compute targeted minimum loss estimate of the interventional direct effect
tmle_de <- medoutcon(W = example_data[, ..w_names],
A = example_data$A,
Z = example_data$Z,
M = example_data[, ..m_names],
Y = example_data$Y,
effect = "direct",
estimator = "tmle")
tmle_de
#> Interventional Direct Effect
#> Estimator: tmle
#> Estimate: -0.06
#> Std. Error: 0.058
#> 95% CI: [-0.173, 0.053]For details on how to use data adaptive regression (machine learning) techniques in the estimation of nuisance parameters, consider consulting the vignette that accompanies the package.
If you encounter any bugs or have any specific feature requests, please file an issue.
Contributions are very welcome. Interested contributors should consult our contribution guidelines prior to submitting a pull request.
After using the medoutcon R package, please cite the following:
@article{diaz2020nonparametric,
title={Non-parametric efficient causal mediation with intermediate
confounders},
author={D{\'\i}az, Iv{\'a}n and Hejazi, Nima S and Rudolph, Kara E
and {van der Laan}, Mark J},
year={2020},
url = {https://arxiv.org/abs/1912.09936},
doi = {10.1093/biomet/asaa085},
journal={Biometrika},
volume = {108},
number = {3},
pages = {627--641},
publisher={Oxford University Press}
}
@article{hejazi2022medoutcon-joss,
author = {Hejazi, Nima S and Rudolph, Kara E and D{\'\i}az,
Iv{\'a}n},
title = {{medoutcon}: Nonparametric efficient causal mediation
analysis with machine learning in {R}},
year = {2022},
doi = {10.21105/joss.03979},
url = {https://doi.org/10.21105/joss.03979},
journal = {Journal of Open Source Software},
publisher = {The Open Journal}
}
@software{hejazi2022medoutcon-rpkg,
author={Hejazi, Nima S and D{\'\i}az, Iv{\'a}n and Rudolph, Kara E},
title = {{medoutcon}: Efficient natural and interventional causal
mediation analysis},
year = {2022},
doi = {10.5281/zenodo.5809519},
url = {https://github.com/nhejazi/medoutcon},
note = {R package version 0.1.6}
}
© 2020-2022 Nima S. Hejazi
The contents of this repository are distributed under the MIT license. See below for details:
MIT License
Copyright (c) 2020-2022 Nima S. Hejazi
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
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Coyle, Jeremy R, Nima S Hejazi, Ivana Malenica, Rachael V Phillips, and
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