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Cram Policy: Efficient Simultaneous Policy Learning and Evaluation

Source: R/cram_policy.R
cram_policy.Rd

This function performs the cram method (simultaneous policy learning and evaluation) for binary policies on data including covariates (X), binary treatment indicator (D) and outcomes (Y).

Usage

cram_policy(
  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,
  alpha = 0.05,
  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 non-treated).

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 "causal_forest", "s_learner", and "m_learner". Default is "causal_forest". Note: you can also set model_type to NULL and specify custom_fit and custom_predict to use your custom model.

learner_type

The learner type for the chosen model. Options include "ridge" for Ridge Regression, "fnn" for Feedforward Neural Network and "caret" for Caret. Default is "ridge". if model_type is 'causal_forest', choose NULL, if model_type is 's_learner' or 'm_learner', choose between 'ridge', 'fnn' and 'caret'.

baseline_policy

A list providing the baseline policy (binary 0 or 1) for each sample. If NULL, defaults to a list of zeros with the same length as the number of rows in X.

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 FALSE.

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 NULL.

custom_fit

A custom, user-defined, function that outputs a fitted model given training data (allows flexibility). Defaults to NULL.

custom_predict

A custom, user-defined, function for making predictions given a fitted model and test data (allow flexibility). Defaults to NULL.

alpha

Significance level for confidence intervals. Default is 0.05 (95% confidence).

propensity

The propensity score function for binary treatment indicator (D) (probability for each unit to receive treatment). Defaults to 0.5 (random assignment).

Value

A list containing:

  • raw_results: A data frame summarizing key metrics with truncated decimals:

    • Delta Estimate: The estimated treatment effect (delta).

    • Delta Standard Error: The standard error of the delta estimate.

    • Delta CI Lower: The lower bound of the confidence interval for delta.

    • Delta CI Upper: The upper bound of the confidence interval for delta.

    • Policy Value Estimate: The estimated policy value.

    • Policy Value Standard Error: The standard error of the policy value estimate.

    • Policy Value CI Lower: The lower bound of the confidence interval for policy value.

    • Policy Value CI Upper: The upper bound of the confidence interval for policy value.

    • Proportion Treated: The proportion of individuals treated under the final policy.

  • interactive_table: An interactive table summarizing key metrics for detailed exploration.

  • final_policy_model: The final fitted policy model based on model_type and learner_type or custom_fit.

See also

causal_forest, cv.glmnet, keras_model_sequential

Examples

# Example data
X_data <- matrix(rnorm(100 * 5), nrow = 100, ncol = 5)
D_data <- as.integer(sample(c(0, 1), 100, replace = TRUE))
Y_data <- rnorm(100)
nb_batch <- 5

# Perform CRAM policy
result <- cram_policy(X = X_data,
                          D = D_data,
                          Y = Y_data,
                          batch = nb_batch)

# Access results
result$raw_results
#>                        Metric    Value
#> 1              Delta Estimate  0.36852
#> 2        Delta Standard Error  0.26573
#> 3              Delta CI Lower -0.15230
#> 4              Delta CI Upper  0.88935
#> 5       Policy Value Estimate  0.53258
#> 6 Policy Value Standard Error  0.27544
#> 7       Policy Value CI Lower -0.00727
#> 8       Policy Value CI Upper  1.07243
#> 9          Proportion Treated  1.00000
result$interactive_table


result$final_policy_model
#> GRF forest object of type causal_forest 
#> Number of trees: 100 
#> Number of training samples: 100 
#> Variable importance: 
#>     1     2     3     4     5 
#> 0.089 0.187 0.142 0.124 0.160