Convenience wrapper around margot_rate_batch(). Returns two data frames, both sorted by descending RATE Estimate, with reliable results highlighted in bold.
Usage
margot_rate(
models,
model_names = NULL,
policy = c("treat_best", "withhold_best"),
round_digits = 3,
highlight_significant = TRUE,
label_mapping = NULL,
remove_tx_prefix = TRUE,
remove_z_suffix = TRUE,
use_title_case = TRUE,
remove_underscores = TRUE,
adjust = "none",
alpha = 0.05,
apply_adjustment = adjust != "none",
seed = 12345,
use_evaluation_subset = TRUE,
target = c("AUTOC", "QINI"),
q = seq(0.1, 1, by = 0.1),
...
)Arguments
- models
List from margot_causal_forest().
- model_names
Optional character vector specifying which models to process. Default NULL (all models).
- policy
Character; "treat_best" (default) or "withhold_best".
- round_digits
Integer; decimal places (default 3).
- highlight_significant
Logical; bold outcomes whose 95 percent CI excludes 0 (default TRUE). Note: Only positive significant effects are bolded, negative effects are not bolded.
- label_mapping
Named character vector for converting variable names to readable labels.
- remove_tx_prefix
Logical; remove treatment prefix from variable names (default TRUE).
- remove_z_suffix
Logical; remove z-score suffix from variable names (default TRUE).
- use_title_case
Logical; convert variable names to title case (default TRUE).
- remove_underscores
Logical; replace underscores with spaces (default TRUE).
- adjust
Character; method for adjusting p-values. Only "bonferroni" or "none" are recommended. While other methods are technically available through stats::p.adjust(), they may not be appropriate for all contexts (e.g., cross-validation). Default is "none". When using Bonferroni, consider alpha = 0.2 due to its conservative nature.
- alpha
Numeric; significance threshold (default 0.05). When using Bonferroni correction with noisy heterogeneous treatment effect models, alpha = 0.2 may be more appropriate to maintain reasonable statistical power.
- apply_adjustment
Logical; if TRUE, compute p-values and apply adjustment method. If FALSE, just document the adjustment method without recomputing. Default is TRUE when adjust parameter is provided, FALSE otherwise.
- seed
Integer; base seed for reproducible RATE computations (default 12345).
- use_evaluation_subset
Logical; if TRUE, use test indices from qini_metadata when available for proper out-of-sample evaluation (default TRUE).
- target
Character vector; weighting schemes to compute: "AUTOC", "QINI", or c("AUTOC", "QINI") for both (default). When a single target is specified, only that table is returned.
- q
Numeric vector of quantiles at which to evaluate. Default is seq(0.1, 1, by = 0.1) which matches the GRF default.
- ...
Additional arguments passed to grf::rank_average_treatment_effect().