d.highdim

<p>These crisp-set data are simulated from a presupposed data generating structure (i.e. a causal chain). They feature 20% noise and massive fragmentation (limited diversity). <code>d.highdim</code> is used to illustrate CNA's capacity to analyze high-dimensional data.</p>

datasets

Provides comprehensive functionalities for causal modeling with Coincidence Analysis (CNA), which is a configurational comparative method of causal data analysis that was first introduced in Baumgartner (2009) <doi:10.1177/0049124109339369>, and generalized in Baumgartner & Ambuehl (2018) <doi:10.1017/psrm.2018.45>. CNA is designed to recover INUS-causation from data, which is particularly relevant for analyzing processes featuring conjunctural causation (component causation) and equifinality (alternative causation). CNA is currently the only method for INUS-discovery that allows for multiple effects (outcomes/endogenous factors), meaning it can analyze common-cause and causal chain structures.

Mathias Ambuehl

Causal Modeling with Coincidence Analysis

Michael Baumgartner

Ruedi Epple

Veli-Pekka Parkkinen

Alrik Thiem

d.highdim function

<p>The data frame contains 50 factors (columns), V1 to V50, and 1191 rows (cases). It was simulated from the following data generating structure:
$$(v2*V10 + V18*V16*v15 &lt;-&gt; V13)*(V2*v14 + V3*v12 + V13*V19 &lt;-&gt; V11)$$
20% of the cases in <code>d.highdim</code> are incompatible with that structure, meaning they are affected by noise or measurement error. The fragmentation is massive, as there is a total of 281 trillion ($2^{48}$) configurations over the set {V1,...,V50} that are compatible with that structure.</p>

Format

Artificial data with 50 factors and 1191 cases — d.highdim

Artificial data with 50 factors and 1191 cases

d.highdim: Artificial data with 50 factors and 1191 cases

Description

Usage

Arguments

Format