causal_quartet

This dataset contains 400 observations, each generated under a different
data generating mechanism:<ul>
<li>(1) A collider</li>
<li>(2) A confounder</li>
<li>(3) A mediator</li>
<li>(4) M-bias</li>
</ul>

datasets

In the spirit of Anscombe's quartet, this package includes datasets
that demonstrate the importance of visualizing your data, the importance of
not relying on statistical summary measures alone, and why additional
assumptions about the data generating mechanism are needed when estimating
causal effects. The package includes "Anscombe's Quartet" (Anscombe 1973) <doi:10.1080/00031305.1973.10478966>,
D'Agostino McGowan & Barrett (2023) "Causal Quartet" <doi:10.48550/arXiv.2304.02683>,
"Datasaurus Dozen" (Matejka & Fitzmaurice 2017), "Interaction Triptych" (Rohrer & Arslan 2021) <doi:10.1177/25152459211007368>, "Rashomon Quartet" (Biecek et al. 2023) <doi:10.48550/arXiv.2302.13356>, and
Gelman "Variation and Heterogeneity Causal Quartets" (Gelman et al. 2023) <doi:10.48550/arXiv.2302.12878>.

Lucy D'Agostino McGowan

quartets

Datasets to Help Teach Statistics

causal_quartet function

A dataframe with 400 rows and 6 variables:<ul>
<li><code>dataset</code>: The data generating mechanism</li>
<li><code>exposure</code>: exposure</li>
<li><code>outcome</code>: outcome</li>
<li><code>covariate</code>: a known factor</li>
<li><code>u1</code>: an unknown factor</li>
<li><code>u2</code>: an unknown factor</li>
</ul>

causal_quartet: Causal Quartet Data

Description

Usage

Arguments

Format

References