@article{JRC124871,
	address = {NETHERLANDS},
	year = {2022},
	author = {Spinu N and Cronin M and Madden J and Worth A},
	abstract = {Computational models in toxicology are mostly based on pattern recognition and less on studying the cause and effect relationships. Quantitative Adverse Outcome Pathway (qAOP) models are promising tools for deciphering mechanistically adverse effects as a result of exposure to chemicals. This commentary explores the role of causality reasoning to support the development of qAOP models for real-world applications and better informed decision making in chemical risk assessment.
		},
	title = {A Matter of Trust: Learning Lessons About Causality Will Make qAOPs Credible},
	type = {Full paper},
	url = {},
	volume = {21},
	number = {},
	journal = {COMPUTATIONAL TOXICOLOGY},
	pages = {100205},
	issn = {2468-1113 (print)},
	publisher = {ELSEVIER BV},
	doi = {10.1016/j.comtox.2021.100205 (online)}
}