train_it

Function to develop prediction model based on user's preferences.

internal

Provides a framework for estimating causal effects of a continuous
exposure using observational data, and implementing matching and weighting
on the generalized propensity score.
Wu, X., Mealli, F., Kioumourtzoglou, M.A., Dominici, F. and Braun, D., 2022.
Matching on generalized propensity scores with continuous exposures. Journal
of the American Statistical Association, pp.1-29.

Naeem Khoshnevis

CausalGPS

Matching on Generalized Propensity Scores with Continuous
Exposures

Xiao Wu

Danielle Braun

train_it function

<dl><dt>target</dt>
<dd>A vector of target data.</dd>
<dt>input</dt>
<dd>A vector, matrix, or dataframe of input data.</dd>
<dt>sl_lib_internal</dt>
<dd>The internal library to be used by SuperLearner</dd>
<dt>...</dt>
<dd>Model related parameters should be provided.</dd></dl>

Arguments

Generate Prediction Model — train_it

<dl>

<dt>target</dt>
<dd>A vector of target data.</dd>


<dt>input</dt>
<dd>A vector, matrix, or dataframe of input data.</dd>


<dt>sl_lib_internal</dt>
<dd>The internal library to be used by SuperLearner</dd>


<dt>...</dt>
<dd>Model related parameters should be provided.</dd>

</dl>

train_it: Generate Prediction Model

Description

Usage

Value

Arguments