Assemble policy-tree plots, table, and standard text

Convenience wrapper for the standard policy-tree reporting artefacts. The returned components are ordinary objects that can be edited, replaced, or omitted in manuscript workflows.

Usage

margot_report_policy_tree(
  result_object,
  model_name,
  policy_cv = NULL,
  depth = NULL,
  original_df = NULL,
  weights = NULL,
  digits = 3L,
  ci_level = 0.95,
  label_mapping = NULL,
  include_plots = TRUE,
  include_table = TRUE,
  include_text = TRUE,
  include_policy_value = !is.null(policy_cv),
  layout = list(heights = c(1, 2)),
  annotation = list(tag_levels = "A"),
  projection_args = list(),
  decision_tree_args = list()
)

Arguments

result_object

A margot_causal_forest()-style object.

model_name

Character scalar naming the model to report.

policy_cv

Optional margot_policy_tree_cv object with held-out policy-tree diagnostics.

depth

Optional integer tree depth. If NULL and policy_cv is supplied, the selected depth from policy_cv$depth_map is used when available; otherwise depth one is used.

original_df

Optional data frame with original-scale variables.

weights

Optional evaluation weights.

digits

Integer; rounding used in formatted table columns.

ci_level

Confidence level for leaf score intervals.

label_mapping

Optional named list used for display labels.

include_plots

Logical. Include plot components.

include_table

Logical. Include the leaf table.

include_text

Logical. Include standard interpretation text.

include_policy_value

Logical. Include held-out value summaries when policy_cv is supplied.

layout

List passed to margot_plot_policy_tree_panels().

annotation

List passed to margot_plot_policy_tree_panels().

projection_args

Optional list of arguments for the projection plot.

decision_tree_args

Optional list of arguments for the decision tree.

Value

A list with table, text, plots, and metadata.

Details

This helper reports display-tree artefacts and can also attach held-out policy-value summaries when supplied a margot_policy_tree_cv object. Leaf tables use the signed evaluation-sample T-C contrast $$ \Delta_L = \frac{\sum_{j \in E_L} w_j\{\Gamma_{jT} - \Gamma_{jC}\}} {\sum_{j \in E_L} w_j} $$ where \(\Gamma_{ja}\) is the action score for observation \(j\) under action \(a\) and \(E_L\) are evaluation observations in leaf \(L\). The selected action is reported separately as the fitted tree's stored action, learned on training observations \(S_L\): $$ \pi(L) = \arg\max_{a \in \{C,T\}} \frac{\sum_{j \in S_L} w_j \Gamma_{ja}}{\sum_{j \in S_L} w_j}. $$ Tree-level value summaries compare the learned rule with all-control, all-treatment, and best-constant baselines. Between-leaf differences in \(\Delta_L\) describe variation in magnitude, not the policy-tree decision rule.