Summer 2018 Causal Inference Workshops

该奖项为新研究人员参加2018年夏季的两次因果推理会议提供部分支持：2018年5月22日至23日在宾夕法尼亚州匹兹堡举行的大西洋因果推理会议（ACIC）和2018年8月6日至10日在加利福尼亚州蒙特雷举行的人工智能不确定性会议（UAI）的因果研讨会。这两次会议都是领导人的旗舰聚会，讨论观察到的影响的原因。 近年来，因果推理取得了重要进展，特别是通过其理论和实践领域的显着扩展。机器学习方法专注于超高维模型和可扩展的随机算法，而更经典的因果推理一直在指导涉及经济学，社会和健康科学以及商业的复杂领域的政策。通过这些进步，一个强大的异花授粉已经出现了一套新的方法，有望提供更强大的数据分析比每个领域可以单独。这两个会议的主要目的是推进因果推理的研究，并弥合不同社区之间的差距在因果推理，通过促进新的研究人员之间的互动和网络。在这两次会议上，支持的参与者将通过邀请和贡献的演讲或通过海报展示来展示他们的研究。由于会议借鉴了统计学，生物统计学，计算机科学和其他学科的因果推理社区，这些会议为新研究人员提供了一个很好的机会，可以看到这一领域研究和合作的机会。 更多信息可在会议网站上获得：ACIC：https://www.cmu.edu/acic2018/ UAI：https://sites.google.com/view/causaluai2018This奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

This award provides partial support for new researchers to attend two conferences on causal inference in summer 2018: The Atlantic Causal Inference Conference (ACIC) held in Pittsburgh, PA on May 22-23, 2018 and the Causal Workshop at the Conference on Uncertainty in Artificial Intelligence (UAI) held in Monterey, CA on August 6-10, 2018. Both conferences serve as flagship gatherings of leaders in reasoning about causes of observed effects. In recent years, causal inference has seen important advances, especially through a dramatic expansion in its theoretical and practical domains. Machine learning methods have focused on ultra-high-dimensional models and scalable stochastic algorithms, whereas more classical causal inference has been guiding 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 more robust data analysis than each field could individually.The primary purpose of the two conferences is to advance the study of causal inference and to bridge gaps between different communities within causal inference, by promoting interaction and networking among new researchers. At both conferences, the supported participants will present their research via invited and contributed talks or via poster presentations. Since the conferences draw on causal inference communities in statistics, biostatistics, computer science, and other disciplines, these conferences present a great opportunity for new researchers to see the breadth of opportunities for research and collaboration in this area. More information is available at the conference web sites:ACIC: https://www.cmu.edu/acic2018/ UAI: https://sites.google.com/view/causaluai2018This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.