validate_params: Cram Policy: Validate User-Provided Parameters for a Model

In cramR: Cram Method for Efficient Simultaneous Learning and Evaluation

Cram Policy: Validate User-Provided Parameters for a Model

Description

This function validates user-provided parameters against the formal arguments of a specified model function. It ensures that all user-specified parameters are recognized by the model and raises an error for invalid parameters.

Usage

validate_params(model_function, model_type, learner_type, user_params)

Arguments

model_function	The model function for which parameters are being validated (e.g., grf::causal_forest).
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'.
user_params	A named list of parameters provided by the user.

Value

A named list of validated parameters that are safe to pass to the model function.

cramR documentation built on Aug. 25, 2025, 1:12 a.m.