Conferences

Conferences and Journals for Causal Inference

因果科学会议

近期因果科学有两个信息值得同步：1）学者崔鹏，张含望老师研讨“VALSE Webinar 21-21期 总第248期 知其所以然：因果推理与学习“ https://mp.weixin.qq.com/s/jyCJAlgL4XdzvRLTqHTreA ；2）因果科学和 causalai 首届专门的会议目前已经开始征稿，Bernhard Scholkopf 等一众大佬主办 https://www.cclear.cc/CallforPapers

Awesome Causal Inference Seminar

因果推断领域主要的会要是 ACIC, EuroCIM, UAI, 然后是其他AI顶级会议的 Workshop. 各个会议的主题分别是：

  • ACIC is an interdisciplinary conference designed to bring together researchers, students, and practitioners of causal inference with emphasis on theory, methodology, and application.

  • EuroCIM aims to provide a forum for people interested in causal inference to meet informally, for early career and established researchers to present and discuss the latest developments in the field, and to offer opportunities for networking to develop future research opportunities and collaborations.

  • (UAI 2018) This workshop is aimed at facilitating more interactions between researchers in machine learning, statistics, and computer science working on questions of causal inference.

  • KDD2019 continues to serve as a forum for researchers and practitioners in data mining and other disciplines to share their recent research in causal discovery in their respective fields and to explore the possibility of interdisciplinary collaborations in the study of causality.

  • (AAI-WHY2019) Our aim is to bring together researchers to discuss the integration of causal, counterfactual, and imagination-based reasoning into data science, building a richer framework for research and a new horizon of applications in the coming decades.

  • (NIPS Causal 2018) The route from machine learning to artificial intelligence remains uncharted. Recent efforts describe some of the conceptual problems that lie along this route. The goal of this workshop is to investigate how much progress is possible by framing these problems beyond learning correlations, that is, by uncovering and leveraging causal relations.

AAAI19-Why

google "aaai 2019 accepted papers" 总共1000(of 7000) 接受的文章，与因果推断相关的文章超出10篇。

该会议的一个workshop： https://why19.causalai.net/ 研究了 causality + X: X 代表某个特定领域，包括 Computer Vision & Imagination， Machine Learning & Artificial Intelligence，the Social Sciences & Economics， the Health Sciences


Causal inference has numerous real-world applications in many domains . However, traditional treatment effect estimation methods may not well handle large-scale and high-dimensional heterogeneous data. In recent years, an emerging research direction has attracted increasing attention in the broad artificial intelligence field, which combines the advantages of traditional treatment effect estimation approaches (e.g., matching estimators) and advanced representation learning approaches (e.g., deep neural networks). In this tutorial, we will

  • introduce both traditional and state-of-the-art representation learning algorithms for treatment effect estimation.

  • Background about causal inference, counterfactuals and matching estimators will be covered as well.

  • We will also showcase promising applications of these methods in different application domains.

“科学中的因果关系”会议系列汇集了哲学家和科学家，探讨了因果关系的各个方面。本系列的第14届会议将重点讨论启发式和因果关系之间的关系。