Panelists: Susan Athey, Konrad Kording, Amit Sharma
Moderator: Emre Kiciman
- In everyday life, understanding the causal mechanics of the world around us seems to be -- at least at first glance --- incredibly intuitive to most people. And yet, like many things that people find to be "common sense", understanding and capturing causality is also often surprisingly difficult to operationalize. My first question for the panel --- could you say a few words about causality and its importance in your areas of study, what excites you, and what are the open challenges you see?
- Konrad, what are some of the things that people do well when reasoning about causality. What do we know about how we do that?
- Susan, your work often involves policy setting and decision making where people must understand the mechanisms underlying a market, or other system. Where do you see people making mistakes? How do the formal analyses you work with complement people's cognitive abilities?
- Amit, in your work integrating causal concepts into computing applications, what are the opportunities you see for new (biologically-inspired) models to have an impact on our algorithms and computing methods?
- Each of you takes a broad cross-disciplinary approach in your research---re-reading through your backgrounds before the panel, I was reminded of the breadth of work here, spanning at least large segments of computer science, neuroscience, and social science. With your perspectives at interdisciplinary boundaries, what are some of the key touchpoints that you see as opportunities at the intersection of natural and artificial intelligence?