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README.Rmd
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README.Rmd
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---
output: github_document
bibliography: inst/references.bib
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
> crumble (verb): break or fall apart into small fragments
# crumble
<!-- badges: start -->
[](https://lifecycle.r-lib.org/articles/stages.html#experimental) [](https://CRAN.R-project.org/package=crumble) [](https://www.gnu.org/licenses/gpl-3.0)
<!-- badges: end -->
*crumble* implements a modern, unified estimation strategy [@liu2024general] for common mediation estimands: natural effects [@pearl2022], organic effects [@lok2015], interventional effects [@vansteelandt2017], recanting twins [@vo2024], in causal inference in combination with modified treatment policies. It makes use of recent advancements in "Riesz-learning" to estimate a set of required nuisance parameters using deep learning. The result is a software package that is capable of estimating mediation effects with binary, categorical, continuous, or multivariate exposures with high-dimensional mediators and mediator-outcome confounders using machine learning.
This work was supported by the National Institute on Drug Abuse [R00DA042127].
### Installation
```{r eval=FALSE}
remotes::install_github("nt-williams/crumble")
```
### Features
| Feature | Status |
|---------------------------|:-------:|
| Recanting twins | ✓ |
| Natural effects | ✓ |
| Organic effects | ✓ |
| Interventional effects | ✓ |
| Modified treatment Policy | ✓ |
| Static intervention | ✓ |
| Dynamic intervention | ✓ |
| Continuous treatment | ✓ |
| Binary treatment | ✓ |
| Categorical treatment | ✓ |
| Multivariate treatment | ✓ |
| Missingness in treatment | |
| Continuous outcome | ✓ |
| Binary outcome | ✓ |
| Censored outcome | ✓ |
| Survey weights | Planned |
| Super learner | ✓ |
| Clustered data | Planned |
| Parallel processing | ✓ |
| GPU support | ✓ |
| Progress bars | ✓ |
### Example(s)
```{r eval=FALSE}
library(crumble)
library(mlr3extralearners)
data(weight_behavior, package = "mma")
weight_behavior <- na.omit(weight_behavior)
set.seed(2345)
```
##### Recanting twins
```{r eval=FALSE}
crumble(
data = weight_behavior,
trt = "sports",
outcome = "bmi",
covar = c("age", "sex", "tvhours"),
mediators = c("exercises", "overweigh"),
moc = "snack",
d0 = \(data, trt) factor(rep(1, nrow(data)), levels = c("1", "2")),
d1 = \(data, trt) factor(rep(2, nrow(data)), levels = c("1", "2")),
effect = "RT",
learners = c("mean", "glm", "earth", "ranger"),
nn_module = sequential_module(),
control = crumble_control(crossfit_folds = 1L, epochs = 20L)
)
#> ✔ Permuting Z-prime variables... 1/1 tasks [2.5s]
#> ✔ Fitting outcome regressions... 1/1 folds [25.6s]
#> ✔ Computing alpha n density ratios... 1/1 folds [39.7s]
#> ✔ Computing alpha r density ratios... 1/1 folds [41.6s]
#>
#> ══ Results `crumble()` ═════════════════════════════════════════
#>
#> ── E[Y(d1) - Y(d0)]
#> Estimate: 1.0537
#> Std. error: 0.3009
#> 95% CI: (0.4639, 1.6435)
#>
#> ── Path: A -> Y
#> Estimate: 0.0366
#> Std. error: 0.1842
#> 95% CI: (-0.3245, 0.3976)
#>
#> ── Path: A -> Z -> Y
#> Estimate: -0.0202
#> Std. error: 0.0238
#> 95% CI: (-0.0668, 0.0264)
#>
#> ── Path: A -> Z -> M -> Y
#> Estimate: -6e-04
#> Std. error: 0.0099
#> 95% CI: (-0.02, 0.0189)
#>
#> ── Path: A -> M -> Y
#> Estimate: 1.0506
#> Std. error: 0.2162
#> 95% CI: (0.627, 1.4743)
#>
#> ── Intermediate Confounding
#> Estimate: -0.0127
#> Std. error: 0.0261
#> 95% CI: (-0.0638, 0.0384)
```
##### Natural effects
```{r eval=FALSE}
crumble(
data = weight_behavior,
trt = "sports",
outcome = "bmi",
covar = c("age", "sex", "tvhours"),
mediators = c("exercises", "overweigh"),
d0 = \(data, trt) factor(rep(1, nrow(data)), levels = c("1", "2")),
d1 = \(data, trt) factor(rep(2, nrow(data)), levels = c("1", "2")),
effect = "N",
learners = c("mean", "glm", "earth", "ranger"),
nn_module = sequential_module(),
control = crumble_control(crossfit_folds = 1L, epochs = 20L)
)
#> ✔ Fitting outcome regressions... 1/1 folds [10.6s]
#> ✔ Computing alpha n density ratios... 1/1 folds [53.1s]
#>
#> ══ Results `crumble()` ═════════════════════════════════════════
#>
#> ── E[Y(d1) - Y(d0)]
#> Estimate: 1.0289
#> Std. error: 0.28
#> 95% CI: (0.48, 1.5777)
#>
#> ── Natural Direct Effect
#> Estimate: 0.0165
#> Std. error: 0.1717
#> 95% CI: (-0.3201, 0.3531)
#>
#> ── Natural Indirect Effect
#> Estimate: 1.0124
#> Std. error: 0.2178
#> 95% CI: (0.5856, 1.4393)
```
##### Organic effects
```{r eval=FALSE}
crumble(
data = weight_behavior,
trt = "sports",
outcome = "bmi",
covar = c("age", "sex", "tvhours"),
mediators = c("exercises", "overweigh"),
d0 = \(data, trt) factor(rep(1, nrow(data)), levels = c("1", "2")),
d1 = \(data, trt) factor(rep(2, nrow(data)), levels = c("1", "2")),
effect = "O",
learners = c("mean", "glm", "earth", "ranger"),
nn_module = sequential_module(),
control = crumble_control(crossfit_folds = 1L, epochs = 20L)
)
#> ✔ Fitting outcome regressions... 1/1 folds [10.7s]
#> ✔ Computing alpha n density ratios... 1/1 folds [48.2s]
#>
#> ══ Results `crumble()` ═════════════════════════════════════════
#>
#> ── Organic Direct Effect
#> Estimate: 0.011
#> Std. error: 0.1772
#> 95% CI: (-0.3364, 0.3584)
#>
#> ── Organic Indirect Effect
#> Estimate: 1.0278
#> Std. error: 0.2231
#> 95% CI: (0.5904, 1.4651)#>
```
##### Randomized interventional effects
```{r eval=FALSE}
crumble(
data = weight_behavior,
trt = "sports",
outcome = "bmi",
covar = c("age", "sex", "tvhours"),
mediators = c("exercises", "overweigh"),
moc = "snack",
d0 = \(data, trt) factor(rep(1, nrow(data)), levels = c("1", "2")),
d1 = \(data, trt) factor(rep(2, nrow(data)), levels = c("1", "2")),
effect = "RI",
learners = c("mean", "glm", "earth", "ranger"),
nn_module = sequential_module(),
control = crumble_control(crossfit_folds = 1L, epochs = 20L)
)
#> ✔ Permuting Z-prime variables... 1/1 tasks [2s]
#> ✔ Fitting outcome regressions... 1/1 folds [14.2s]
#> ✔ Computing alpha r density ratios... 1/1 folds [1m 23.2s]
#>
#> ══ Results `crumble()` ═════════════════════════════════════════
#>
#> ── Randomized Direct Effect
#> Estimate: 0.0162
#> Std. error: 0.1774
#> 95% CI: (-0.3315, 0.364)
#>
#> ── Randomized Indirect Effect
#> Estimate: 1.0304
#> Std. error: 0.2296
#> 95% CI: (0.5805, 0.4662)
```
#### References