validate_params

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.

Performs the Cram method, a general and efficient approach to simultaneous learning and evaluation using a generic machine learning algorithm. In a single pass of batched data, the proposed method repeatedly trains a machine learning algorithm and tests its empirical performance. Because it utilizes the entire sample for both learning and evaluation, cramming is significantly more data-efficient than sample-splitting. Unlike cross-validation, Cram evaluates the final learned model directly, providing sharper inference aligned with real-world deployment. The method naturally applies to both policy learning and contextual bandits, where decisions are based on individual features to maximize outcomes. The package includes cram_policy() for learning and evaluating individualized binary treatment rules, cram_ml() to train and assess the population-level performance of machine learning models, and cram_bandit() for on-policy evaluation of contextual bandit algorithms. For all three functions, the package provides estimates of the average outcome that would result if the model were deployed, along with standard errors and confidence intervals for these estimates. Details of the method are described in Jia, Imai, and Li (2024) <https://www.hbs.edu/ris/Publication%20Files/2403.07031v1_a83462e0-145b-4675-99d5-9754aa65d786.pdf> and Jia et al. (2025) <doi:10.48550/arXiv.2403.07031>.

Yanis Vandecasteele

cramR

Cram Method for Efficient Simultaneous Learning and Evaluation

validate_params function

<dl><dt>model_function</dt>
<dd>The model function for which parameters are being validated (e.g., <code>grf::causal_forest</code>).</dd>
<dt>model_type</dt>
<dd>The model type for policy learning. Options include <code>"causal_forest"</code>, <code>"s_learner"</code>, and <code>"m_learner"</code>. Default is <code>"causal_forest"</code>. Note: you can also set model_type to NULL and specify custom_fit and custom_predict to use your custom model.</dd>
<dt>learner_type</dt>
<dd>The learner type for the chosen model. Options include <code>"ridge"</code> for Ridge Regression, <code>"fnn"</code> for Feedforward Neural Network and <code>"caret"</code> for Caret. Default is <code>"ridge"</code>. if model_type is 'causal_forest', choose NULL, if model_type is 's_learner' or 'm_learner', choose between 'ridge', 'fnn' and 'caret'.</dd>
<dt>user_params</dt>
<dd>A named list of parameters provided by the user.</dd></dl>

Arguments

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

<dl>

<dt>model_function</dt>
<dd>The model function for which parameters are being validated (e.g., <code>grf::causal_forest</code>).</dd>


<dt>model_type</dt>
<dd>The model type for policy learning. Options include <code>"causal_forest"</code>, <code>"s_learner"</code>, and <code>"m_learner"</code>. Default is <code>"causal_forest"</code>. Note: you can also set model_type to NULL and specify custom_fit and custom_predict to use your custom model.</dd>


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


<dt>user_params</dt>
<dd>A named list of parameters provided by the user.</dd>

</dl>

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

Description

Usage

Value

Arguments