Introduction
Micro-randomized trials (MRTs) are designed to evaluate the effectiveness of mobile health (mHealth) interventions delivered via smartphones. In practice, the assumptions required for MRTs are often difficult to satisfy: randomization probabilities can be uncertain, observations are frequently incomplete, and prespecifying features from high-dimensional contexts for linear working models is also challenging. To address these issues, the doubly robust weighted centered least squares (DR-WCLS) framework provides a flexible procedure for variable selection and inference. The methods incorporates supervised learning algorithms and enables valid inference on time-varying causal effects in longitudinal settings.
Set-up
To configure a Python virtual environment in R, please run the following code:
library(MRTpostInfLASSO)
# Configure virtual environment
venv_info = venv_config()
venv = venv_info$hash
print(venv)
# [1] "a9c268bc"
# Do the python deps load?
library(reticulate)
np = import("numpy", convert = FALSE)
lasso_mod = import("selectinf.randomized.lasso", convert = FALSE)$lasso
# Simple test
run_simple_lasso_test_snigdha_fixed(venv)Or, if already configured, use
library(reticulate)
use_virtualenv("a9c268bc", required = TRUE)
np = import("numpy", convert = FALSE)
lassopy = import("selectinf.randomized.lasso", convert = FALSE)$lasso
selected_targets = import("selectinf.base", convert = FALSE)$selected_targets
const = lassopy$gaussian
exact_grid_inference = import("selectinf.randomized.exact_reference", convert = FALSE)$exact_grid_inference Detailed tutorial
A detailed tutorial and parameter explanation can be found here https://whd-lab.github.io/DR_WCLS_LASSO/index.html .