2022-06-28-mcduff22a.md

abstract	booktitle	title	year	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
The ability to perform causal and counterfactual reasoning are central properties of human intelligence. Decision-making systems that can perform these types of reasoning have the potential to be more generalizable and interpretable. Simulations have helped advance the state-of-the-art in this domain, by providing the ability to systematically vary parameters (e.g., confounders) and generate examples of the outcomes in the case of counterfactual scenarios. However, simulating complex temporal causal events in multi-agent scenarios, such as those that exist in driving and vehicle navigation, is challenging. To help address this, we present a high-fidelity simulation environment that is designed for developing algorithms for causal discovery and counterfactual reasoning in the safety-critical context. A core component of our work is to introduce agency, such that it is simple to define and create complex scenarios using high-level definitions. The vehicles then operate with agency to complete these objectives, meaning low-level behaviors need only be controlled if necessary. We perform experiments with three state-of-the-art methods to create baselines and highlight the affordances of this environment. Finally, we highlight challenges and opportunities for future work.	First Conference on Causal Learning and Reasoning	CausalCity: Complex Simulations with Agency for Causal Discovery and Reasoning	2022	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	mcduff22a	0	CausalCity: Complex Simulations with Agency for Causal Discovery and Reasoning	559	575	559-575	559	false	McDuff, Daniel and Song, Yale and Lee, Jiyoung and Vineet, Vibhav and Vemprala, Sai and Gyde, Nicholas Alexander and Salman, Hadi and Ma, Shuang and Sohn, Kwanghoon and Kapoor, Ashish	given family Daniel McDuff given family Yale Song given family Jiyoung Lee given family Vibhav Vineet given family Sai Vemprala given family Nicholas Alexander Gyde given family Hadi Salman given family Shuang Ma given family Kwanghoon Sohn given family Ashish Kapoor	2022-06-28		Proceedings of the First Conference on Causal Learning and Reasoning	177	inproceedings	date-parts 2022 6 28	https://proceedings.mlr.press/v177/mcduff22a/mcduff22a.pdf