R/medshift

Causal Mediation Analysis for Stochastic Interventions

Authors: Nima Hejazi and Iván
Díaz

What’s medshift?

The medshift R package is designed to provide facilities for
estimating a parameter that arises in a decomposition of the population
intervention causal effect into the (in)direct effects under stochastic
interventions in the setting of mediation analysis. medshift is
designed as an implementation to accompany the methodology described in
Dı́az and Hejazi (2020). Implemented estimators include the classical
substitution (G-computation) estimator, an inverse probability weighted
(IPW) estimator, an efficient one-step estimator using cross-fitting
(Pfanzagl and Wefelmeyer 1985; Zheng and van der Laan 2011; Chernozhukov
et al. 2018), and a cross-validated targeted minimum loss (TML)
estimator (van der Laan and Rose 2011; Zheng and van der Laan 2011).
medshift integrates with the sl3 R
package (Coyle et al. 2022) to allow
constructed estimators to leverage machine learning for nuisance
estimation.

Installation

Install the most recent version from the master branch on GitHub via
remotes:

remotes::install_github("nhejazi/medshift")

Example

To illustrate how medshift may be used to estimate the effect of
applying a stochastic intervention to the treatment (A) while keeping
the mediator(s) (Z) fixed, consider the following example:

library(data.table)
library(medshift)
# produces a simple data set based on ca causal model with mediation
make_simple_mediation_data <- function(n_obs = 1000) {
  # baseline covariate -- simple, binary
  W <- rbinom(n_obs, 1, prob = 0.50)
  # create treatment based on baseline W
  A <- as.numeric(rbinom(n_obs, 1, prob = W / 4 + 0.1))
  # single mediator to affect the outcome
  z1_prob <- 1 - plogis((A^2 + W) / (A + W^3 + 0.5))
  Z <- rbinom(n_obs, 1, prob = z1_prob)
  # create outcome as a linear function of A, W + white noise
  Y <- Z + A - 0.1 * W + rnorm(n_obs, mean = 0, sd = 0.25)
  # full data structure
  data <- as.data.table(cbind(Y, Z, A, W))
  setnames(data, c("Y", "Z", "A", "W"))
  return(data)
}
# set seed and simulate example data
set.seed(75681)
example_data <- make_simple_mediation_data()
# compute one-step estimate for an incremental propensity score intervention
# that triples (delta = 3) the individual-specific odds of receiving treatment
os_medshift <- medshift(W = example_data$W, A = example_data$A,
                        Z = example_data$Z, Y = example_data$Y,
                        delta = 3, estimator = "onestep",
                        estimator_args = list(cv_folds = 3))
summary(os_medshift)
#>      lwr_ci   param_est      upr_ci   param_var    eif_mean   estimator 
#>      0.7401    0.788136    0.836172    0.000601 1.64686e-17     onestep

For details on how to use data adaptive regression (machine learning)
techniques in the estimation of nuisance parameters, consider consulting
the vignette that accompanies this package.

Issues

If you encounter any bugs or have any specific feature requests, please
file an issue.

Contributions

Contributions are very welcome. Interested contributors should consult
our contribution
guidelines
prior to submitting a pull request.

Citation

After using the medshift R package, please cite the following:

    @article{diaz2020causal,
      title={Causal mediation analysis for stochastic interventions},
      author={D{\'\i}az, Iv{\'a}n and Hejazi, Nima S},
      year={2020},
      url = {https://doi.org/10.1111/rssb.12362},
      doi = {10.1111/rssb.12362},
      journal={Journal of the Royal Statistical Society: Series B
        (Statistical Methodology)},
      volume={},
      number={},
      pages={},
      publisher={Wiley Online Library}
    }
    @manual{hejazi2020medshift,
      author = {Hejazi, Nima S and D{\'\i}az, Iv{\'a}n},
      title = {{medshift}: Causal mediation analysis for stochastic
        interventions},
      year  = {2020},
      url = {https://github.com/nhejazi/medshift},
      note = {R package version 0.1.4}
    }

License

© 2018-2022 Nima S. Hejazi

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

MIT License
Copyright (c) 2018-2022 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