Flexible and General Mediation Analysis Using Riesz Representers.

crumble (verb): break or fall apart into small fragments

crumble

crumble implements a modern, unified estimation strategy (Liu et al. 2024) 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.

This work was supported by the National Institute on Drug Abuse [R00DA042127].

Installation

remotes::install_github("nt-williams/crumble")

Features

Example(s)

library(crumble)
library(mlr3extralearners)

data(weight_behavior, package = "mma")

weight_behavior <- na.omit(weight_behavior)

set.seed(2345)

Recanting twins

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

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

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

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