medoutcon

medoutcon: Nonparametric efficient causal mediation analysis with machine learning in R - Published in JOSS (2022)

https://github.com/nhejazi/medoutcon

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:package: R/medoutcon: Efficient Causal Mediation Analysis with Natural and Interventional Direct/Indirect Effects

README.Rmd

---
output:
  rmarkdown::github_document
bibliography: "inst/REFERENCES.bib"
---



```{r, echo = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "README-"
)
```

# R/`medoutcon`


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> Efficient Causal Mediation Analysis for the Natural and Interventional Effects

__Authors:__ [Nima Hejazi](https://nimahejazi.org), [Iván
Díaz](https://idiaz.xyz), and [Kara
Rudolph](https://kararudolph.github.io/)

---

## What's `medoutcon`?

The `medoutcon` R package provides facilities for efficient estimation of
path-specific (in)direct effects that measure the impact of a treatment variable
$A$ on an outcome variable $Y$, through a direct path (through $A$ only) and an
indirect path (through a set of mediators $M$ only). In the presence of an
intermediate mediator-outcome confounder $Z$, itself
affected by the treatment $A$, these correspond to the _interventional_
(in)direct effects described by @diaz2020nonparametric, though similar (yet less
general) effect definitions and/or estimation strategies have appeared i`n
@`vanderweele2014effect, @rudolph2017robust, @zheng2017longitudinal, and
@benkeser2020nonparametric. When no intermediate confounders are present, these
effect definitions simplify to the well-studied _natural_ (in)direct effects,
and our estimators are analogs of those formulated by @zheng2012targeted.  Both
an efficient one-step bias-corrected estimator with cross-fitting
[@pfanzagl1985contributions; @zheng2011cross; @chernozhukov2018double] and a
cross-validated targeted minimum loss estimator (TMLE) [@vdl2011targeted;
@zheng2011cross] are made available. `medoutcon` integrates with the [`sl3` R
package](https://github.com/tlverse/sl3) [@coyle-gh-sl3] to leverage statistical
machine learning in the estimation procedure.

---

## Installation

Install the most recent _stable release_ from GitHub via
[`remotes`](https://CRAN.R-project.org/package=remotes):

```{r gh-master-installation, eval=FALSE}
remotes::install_github("nhejazi/medoutcon")
```

---

## Example

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:

```{r example, warning=FALSE}
library(data.table)
library(stringr)
library(medoutcon)
set.seed(02138)

# 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(n_obs = 5000L)
w_names <- str_subset(colnames(example_data), "W")
m_names <- str_subset(colnames(example_data), "M")

# quick look at the data
head(example_data)

# 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

# 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
```

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.

---

## Issues

If you encounter any bugs or have any specific feature requests, please [file an
issue](https://github.com/nhejazi/medoutcon/issues).

---

## Contributions

Contributions are very welcome. Interested contributors should consult our
[contribution
guidelines](https://github.com/nhejazi/medoutcon/blob/master/CONTRIBUTING.md)
prior to submitting a pull request.

---

## Citation

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  = {2024},
          doi = {10.5281/zenodo.5809519},
          url = {https://github.com/nhejazi/medoutcon},
          note = {R package version 0.2.3}
        }

---

## License

© 2020-2024 [Nima S. Hejazi](https://nimahejazi.org)

The contents of this repository are distributed under the MIT license. See below
for details:
```
MIT License

Copyright (c) 2020-2024 Nima S. Hejazi

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
```

---

## References

Owner

JOSS Publication

medoutcon: Nonparametric efficient causal mediation analysis with machine learning in R

Published

January 05, 2022

DOI

10.21105/joss.03979

Volume 7, Issue 69, Page 3979

Authors

Nima S. Hejazi
Division of Biostatistics, Department of Population Health Sciences, Weill Cornell Medicine, USA

Kara E. Rudolph
Department of Epidemiology, Mailman School of Public Health, Columbia University, USA

Iván Díaz
Division of Biostatistics, Department of Population Health Sciences, Weill Cornell Medicine, USA

Editor

Mikkel Meyer Andersen

Tags

causal inference machine learning semiparametric estimation mediation analysis natural direct effect interventional direct effect

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