Interpret Policy Tree Results — margot_interpret_policy

This function creates an interpretation of policy tree results from a causal forest or multi-arm causal forest model. It generates a formatted description of the policy tree, including the main splits and recommended actions.

Usage

margot_interpret_policy_tree(
  model,
  model_name,
  max_depth = 2L,
  train_proportion = 0.5,
  custom_action_names = NULL,
  label_mapping = NULL,
  original_df = NULL,
  remove_tx_prefix = TRUE,
  remove_z_suffix = TRUE,
  use_title_case = TRUE
)

Arguments

model: A list containing the results from a multi-arm causal forest model.
model_name: A string specifying which model's results to interpret.
max_depth: Integer, 1 or 2; which stored tree to interpret.
train_proportion: Numeric value between 0 and 1 for the proportion of data used for training. Default is 0.5.
custom_action_names: Optional vector of custom names for the actions. Must match the number of actions in the policy tree.
label_mapping: Optional list that maps variable names to custom labels.
original_df: Optional dataframe with untransformed variables, used to display split values on the data scale.
remove_tx_prefix: Logical indicating whether to remove prefixes like t0_ from variable names. Default is TRUE.
remove_z_suffix: Logical indicating whether to remove the _z suffix from variable names. Default is TRUE.
use_title_case: Logical indicating whether to convert variable names to title case. Default is TRUE.

Value

Invisibly returns a string containing the interpretation; also prints it to the console.