init_params

This function is required to be run first before any other function can run.
This will set within the package the global outcome, treatment, and covariate functions for each model to use.

Provides an array of statistical models common in causal inference such as
standardization, IP weighting, propensity matching, outcome regression, and doubly-robust
estimators. Estimates of the average treatment effects from each model are given with the
standard error and a 95% Wald confidence interval (Hernan, Robins (2020) <https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/>).

Joshua Anderson

CausalModels

Causal Inference Modeling for Estimation of Causal Effects

Cyril Rakovski

Yesha Patel

Erin Lee

init_params function

<dl><dt>outcome</dt>
<dd>the outcome variable of interest (must be continuous).</dd>
<dt>treatment</dt>
<dd>the treatment with the causal effect of interest on the outcome.</dd>
<dt>covariates</dt>
<dd>a list/vector of covariate names to be use for confounding adjustment.</dd>
<dt>data</dt>
<dd>a data frame containing the variables in the model.</dd>
<dt>simple</dt>
<dd>a boolean indicator to build default formula with interactions.
If true, interactions will be excluded. If false, interactions will be included. By
default, simple is set to false.</dd></dl>

Arguments

Initialize CausalModels Package — init_params

<dl>

<dt>outcome</dt>
<dd>the outcome variable of interest (must be continuous).</dd>


<dt>treatment</dt>
<dd>the treatment with the causal effect of interest on the outcome.</dd>


<dt>covariates</dt>
<dd>a list/vector of covariate names to be use for confounding adjustment.</dd>


<dt>data</dt>
<dd>a data frame containing the variables in the model.</dd>


<dt>simple</dt>
<dd>a boolean indicator to build default formula with interactions.
If true, interactions will be excluded. If false, interactions will be included. By
default, simple is set to false.</dd>

</dl>

init_params: Initialize CausalModels Package

Description

Usage

Value

Arguments