In recent years, causal inference has seen important advances, especially through a dramatic expansion in its theoretical and practical domains. By assuming a central role in decision making, causal inference has induced interest from computer science, statistics, and machine learning, each field contributing a fresh and unique perspective.

More specifically, computer science has focused on the algorithmic understanding of causality, and general conditions under which causal structures may be inferred. Machine learning methods have focused on high-dimensional models and non-parametric methods, when more more causal inference has been provided policy In complex domains involving economics, social and health sciences, and business. Through such advances a powerful cross-pollination has emerged as a new set of methodologies promising to deliver robust data analysis than each field could individually -- some examples include concepts such as doubly -robust methods, targeted learning, double machine learning, causal trees, all of which have been recently introduced.

This workshop is aimed at facilitating more interactions between researchers in machine learning, statistics, and computer science working on questions of causal inference. In particular, it is an opportunity to bring together highly highly technical individuals who who are strongly motivated by the practicality and real- World impact of their work. Cultivating such interactions will lead to the development of theory, methodology, and - most importantly - practical tools, that better target causal questions across different domains.