fit_model

This function trains a given unfitted model with the provided data and parameters,
according to model type and learner type.

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

fit_model function

<dl><dt>model</dt>
<dd>An unfitted model object, as returned by `set_model`.</dd>
<dt>X</dt>
<dd>A matrix or data frame of covariates for the samples.</dd>
<dt>Y</dt>
<dd>A vector of outcome values.</dd>
<dt>D</dt>
<dd>A vector of binary treatment indicators (1 for treated, 0 for untreated).</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>.</dd>
<dt>learner_type</dt>
<dd>The learner type for the chosen model. Options include <code>"ridge"</code> for Ridge Regression and <code>"fnn"</code> for Feedforward Neural Network. Default is <code>"ridge"</code>.</dd>
<dt>model_params</dt>
<dd>A list of additional parameters to pass to the model, which can be any parameter defined in the model reference package. Defaults to <code>NULL</code>.</dd>
<dt>propensity</dt>
<dd>The propensity score</dd></dl>

Arguments

Cram Policy: Fit Model — fit_model

<dl>

<dt>model</dt>
<dd>An unfitted model object, as returned by `set_model`.</dd>


<dt>X</dt>
<dd>A matrix or data frame of covariates for the samples.</dd>


<dt>Y</dt>
<dd>A vector of outcome values.</dd>


<dt>D</dt>
<dd>A vector of binary treatment indicators (1 for treated, 0 for untreated).</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>.</dd>


<dt>learner_type</dt>
<dd>The learner type for the chosen model. Options include <code>"ridge"</code> for Ridge Regression and <code>"fnn"</code> for Feedforward Neural Network. Default is <code>"ridge"</code>.</dd>


<dt>model_params</dt>
<dd>A list of additional parameters to pass to the model, which can be any parameter defined in the model reference package. Defaults to <code>NULL</code>.</dd>


<dt>propensity</dt>
<dd>The propensity score</dd>

</dl>

fit_model: Cram Policy: Fit Model

Description

Usage

Value

Arguments