  Causal datasets play a critical role in advancing the field of causality.
However, existing datasets often lack the complexity of real-world issues such
as selection bias, unfaithful data, and confounding. To address this gap, we
propose a new synthetic causal dataset, the Structurally Complex with Additive
paRent causalitY (SCARY) dataset, which includes the following features. The
dataset comprises 40 scenarios, each generated with three different seeds,
allowing researchers to leverage relevant subsets of the dataset. Additionally,
we use two different data generation mechanisms for generating the causal
relationship between parents and child nodes, including linear and mixed causal
mechanisms with multiple sub-types. Our dataset generator is inspired by the
Causal Discovery Toolbox and generates only additive models. The dataset has a
Varsortability of 0.5. Our SCARY dataset provides a valuable resource for
researchers to explore causal discovery under more realistic scenarios. The
dataset is available at https://github.com/JayJayc/SCARY.
