rfherrerac/lmtp

:package: Non-parametric Causal Effects of Feasible Interventions Based on Modified Treatment Policies :crystal_ball:

lmtp

Non-parametric Causal Effects of Feasible Interventions Based on Modified Treatment Policies

Nick Williams and Ivan Diaz

---

Installation

lmtp can be installed from GitHub with:

devtools::install_github("nt-williams/lmtp")

The stable, development version can be installed from GitHub with:

devtools::install_github("nt-williams/lmtp@devel")

Scope

lmtp is an R package that provides an estimation framework for the casual effects of feasible interventions based on point-treatment and longitudinal modified treatment policies as described in Diaz, Williams, Hoffman, and Schenck (2020). Two primary estimators are supported, a targeted maximum likelihood (TML) estimator and a sequentially doubly robust (SDR) estimator (a G-computation and an inverse probability of treatment weighting estimator are provided for the sake of being thorough but their use is recommended against in favor of the TML and SDR estimators). Both binary and continuous outcomes (both with censoring) are allowed. lmtp is built atop the sl3 package to utilize ensemble machine learning for estimation. The treatment mechanism is estimated via a density ratio classification procedure irrespective of treatment variable type providing decreased computation time when treatment is continuous.

For an in-depth look at the package’s functionality, please consult the accompanying vignette.

Features

Feature	Status
Point treatment	✓
Longitudinal treatment	✓
Modified treatment intervention	✓
Static intervention	✓
Dynamic intervention	Planned
Continuous treatment	✓
Binary treatment	✓
Categorical treatment	✓
Missingness in treatment
Continuous outcome	✓
Binary outcome	✓
Censored outcome	✓
Mediation
Super learner	✓
Clustered data	Planned
Parallel processing	✓
Progress bars	✓

Example

library(lmtp)
#> lmtp: Causal Effects of Feasible Interventions Based on Modified
#> Treatment Policies
#> Version: 0.0.1.9000
#> 
library(sl3)
library(future)

# the data: 4 treatment nodes with time varying covariates and a binary outcome
head(sim_t4)
#>   ID L_1 A_1 L_2 A_2 L_3 A_3 L_4 A_4 Y
#> 1  1   2   3   0   1   1   1   1   3 0
#> 2  2   2   1   1   4   0   3   1   2 0
#> 3  3   1   0   1   3   1   2   1   1 1
#> 4  4   1   0   0   3   1   3   1   2 0
#> 5  5   3   3   1   1   0   1   1   2 0
#> 6  6   1   0   0   2   0   3   1   4 0

We’re interested in a treatment policy, d, where exposure is decreased by 1 only among subjects whose exposure won’t go below 1 if intervened upon. The true population outcome under this policy is about 0.305.

# our treatment policy function to be applied at all time points
d <- function(a) {
  (a - 1) * (a - 1 >= 1) + a * (a - 1 < 1)
}

In addition to specifying a treatment policy, we need to specify our treatment variables, time-varying covariates, and the sl3 learners to be used in estimation.

# our treatment nodes, a character vector of length 4
a <- c("A_1", "A_2", "A_3", "A_4")
# our time varying nodes, a list of length 4
time_varying <- list(c("L_1"), c("L_2"), c("L_3"), c("L_4"))
# our sl3 learner stack: the mean, GLM, and random forest
lrnrs <- make_learner_stack(Lrnr_mean, 
                            Lrnr_glm, 
                            Lrnr_ranger)

We can now estimate the effect of our treatment policy, d. In this example, we’ll use the cross-validated TML estimator with 10 folds. To speed up computation, we can use parallel processing supported by the future package.

plan(multiprocess)

lmtp_tmle(sim_t4, a, "Y", time_vary = time_varying, k = 0, shift = d, 
          learners_outcome = lrnrs, learners_trt = lrnrs, folds = 10)
# LMTP Estimator: TMLE
#    Trt. Policy: (d)
# 
# Population intervention effect
#       Estimate: 0.2901
#     Std. error: 0.0119
#         95% CI: (0.2667, 0.3134)

Similiar Implementations

A variety of other R packages perform similar tasks as lmtp. However, lmtp is the only R package currently capable of estimating causal effects for binary, categorical, and continuous exposures in both the point treatment and longitudinal setting using traditional causal effects or modified treatment policies.

• txshift
• tmle3
• tmle3shift
• ltmle
• tmle

Citation

Please cite the following when using lmtp in publications. Citation should include both the R package and the paper establishing the statistical methodology.

@Manual{,
    title = {lmtp: {Non}-parametric {Causal} {Effects} of {Feasible} {Interventions} {Based} on {Modified} {Treatment} {Policies}},
    author = {Nicholas T Williams and Iván Díaz},
    year = {2020},
    note = {R package version 0.0.91},
    doi = {10.5281/zenodo.3874931}, 
    url = {https://github.com/nt-williams/lmtp}
}

@Article{,
    journal = {arxiv},
    title = {Non-parametric causal effects based on longitudinal modified treatment policies},
    author = {Iván Díaz and Nicholas Williams and Katherine L Hoffman and Edward J Schneck},
    year = {2020},
    url = {https://arxiv.org/abs/2006.01366}
}

References

Diaz I, Williams N, Hoffman KL, Schenck, EJ (2020). Non-Parametric Causal Effects Based on Longitudinal Modified Treatment Policies. https://arxiv.org/abs/2006.01366