  Causal structure learning (CSL) refers to the task of learning causal
relationships from data. Advances in CSL now allow learning of causal graphs in
diverse application domains, which has the potential to facilitate data-driven
causal decision-making. Real-world CSL performance depends on a number of
$\textit{context-specific}$ factors, including context-specific data
distributions and non-linear dependencies, that are important in practical
use-cases. However, our understanding of how to assess and select CSL methods
in specific contexts remains limited. To address this gap, we present
$\textit{CausalRegNet}$, a multiplicative effect structural causal model that
allows for generating observational and interventional data incorporating
context-specific properties, with a focus on the setting of gene perturbation
experiments. Using real-world gene perturbation data, we show that CausalRegNet
generates accurate distributions and scales far better than current simulation
frameworks. We illustrate the use of CausalRegNet in assessing CSL methods in
the context of interventional experiments in biology.
