crumble (verb): break or fall apart into small fragments
crumble implements a modern, unified estimation strategy for common mediation estimands: natural effects (Pearl 2022), organic effects (Lok 2015), interventional effects (Vansteelandt and Daniel 2017), recanting twins (Vo et al. 2024), 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.
remotes::install_github("nt-williams/crumble")| 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 | ✓ |
library(crumble)
library(mlr3extralearners)
data(weight_behavior, package = "mma")
weight_behavior <- na.omit(weight_behavior)
set.seed(2345)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)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)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)#> 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)