cram_learning {cramR} | R Documentation |
Cram Policy Learning
Description
This function performs the learning part of the Cram Policy method.
Usage
cram_learning(
X,
D,
Y,
batch,
model_type = "causal_forest",
learner_type = "ridge",
baseline_policy = NULL,
parallelize_batch = FALSE,
model_params = NULL,
custom_fit = NULL,
custom_predict = NULL,
n_cores = detectCores() - 1,
propensity = NULL
)
Arguments
X |
A matrix or data frame of covariates for each sample. |
D |
A vector of binary treatment indicators (1 for treated, 0 for untreated). |
Y |
A vector of outcome values for each sample. |
batch |
Either an integer specifying the number of batches (which will be created by random sampling) or a vector of length equal to the sample size providing the batch assignment (index) for each individual in the sample. |
model_type |
The model type for policy learning. Options include |
learner_type |
The learner type for the chosen model. Options include |
baseline_policy |
A list providing the baseline policy (binary 0 or 1) for each sample. If |
parallelize_batch |
Logical. Whether to parallelize batch processing (i.e. the cram method learns T policies, with T the number of batches. They are learned in parallel when parallelize_batch is TRUE vs. learned sequentially using the efficient data.table structure when parallelize_batch is FALSE, recommended for light weight training). Defaults to |
model_params |
A list of additional parameters to pass to the model, which can be any parameter defined in the model reference package. Defaults to |
custom_fit |
A custom, user-defined, function that outputs a fitted model given training data (allows flexibility). Defaults to |
custom_predict |
A custom, user-defined, function for making predictions given a fitted model and test data (allow flexibility). Defaults to |
n_cores |
Number of cores to use for parallelization when parallelize_batch is set to TRUE. Defaults to detectCores() - 1. |
propensity |
The propensity score |
Value
A list containing:
final_policy_model |
The final fitted policy model, depending on |
policies |
A matrix of learned policies, where each column represents a batch's learned policy and the first column is the baseline policy. |
batch_indices |
The indices for each batch, either as generated (if |
See Also
causal_forest
, cv.glmnet
, keras_model_sequential