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https://github.com/nhejazi/medshift

:package: :game_die: R/medshift: Causal Mediation Analysis for Stochastic Interventions
https://github.com/nhejazi/medshift

causal-inference inverse-probability-weights machine-learning mediation-analysis stochastic-interventions targeted-learning treatment-effects

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:package: :game_die: R/medshift: Causal Mediation Analysis for Stochastic Interventions

Awesome Lists containing this project

README 

        
---

output:

  rmarkdown::github_document

bibliography: "inst/REFERENCES.bib"

---



```{r, echo = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "README-"

)

```



# R/`medshift`



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> Causal Mediation Analysis for Stochastic Interventions



__Authors:__ [Nima Hejazi](https://nimahejazi.org) and [Iván

Díaz](https://idiaz.xyz)



---



## 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 @diaz2020causal. Implemented estimators include the

classical substitution (G-computation) estimator, an inverse probability

weighted (IPW) estimator, an efficient one-step estimator using cross-fitting

[@pfanzagl1985contributions; @zheng2011cross; @chernozhukov2018double], and a

cross-validated targeted minimum loss (TML) estimator [@vdl2011targeted;

@zheng2011cross]. `medshift` integrates with the [`sl3` R

package](https://github.com/tlverse/sl3) [@coyle-gh-sl3] 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`](https://CRAN.R-project.org/package=remotes):



```{r gh-master-installation, eval=FALSE}

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:



```{r simple_example, message=FALSE, warning=FALSE}

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)

```



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](https://github.com/nhejazi/medshift/issues).



---



## Contributions



Contributions are very welcome. Interested contributors should consult our

[contribution

guidelines](https://github.com/nhejazi/medshift/blob/master/CONTRIBUTING.md)

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](https://nimahejazi.org)



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