README.Rmd

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# 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

&copy; 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