Many questions in not only social science but AI/ML research are causal in nature: what would happen to individuals, organizations, or the society, if part of their environment were changed? — for example, the impact (or lack thereof) of increases in the minimum wage on employment, the effect of introducing a new AI algorithm on product sales, or the effect of data-driven decision-making on firm performance. Causal inference encompasses statistical (econometric) and computational methods for studying such questions and determining what causes what using data (in particular, observational data).
The Korea Summer Workshop on Causal Inference is an online seminar series to present research and best practices for causal inference, which is prepared for Korean researchers and data scientists. This year, the workshop is geared toward asynchronous virtual lectures covering state-of-the-art causal inference methods.
빅데이터의 홍수 속에서 원인과 결과에 대한 체계적인 분석은 여느 때보다 더 중요해지고 있습니다. 인과추론 방법론의 활용은 모든 학문 분야에서 빠르게 확대되고 있으며, 단순한 A/B 테스트를 넘어 다양한 인과추론 기법을 도입하는 기업들도 점차 늘어나고 있습니다.
Korea Summer Workshop on Causal Inference 는 인과추론 방법론에 대한 이해를 돕고 인과추론에 대한 저변을 확대하기 위해 기획된 온라인 워크샵입니다. 올해는 예년과는 달리, 인과추론 방법론에 관한 온라인 특강들로 구성될 예정입니다. 모든 영상(한국어)은 유튜브 채널에 업로드 될 예정이며, 강의자료(영어)는 교육 목적으로 자유롭게 활용할 수 있도록 PPT 형태로 공유됩니다.
The workshop this year will be replaced with semester-long lectures, which are designed to be a 3-credit course on causal inference for empirical research at the graduate level.
All lecture notes in PPT can be used for educational purposes, with proper citation.
All lectures will be delivered asynchronously in Korean and uploaded on the YouTube Channel. For learning materials, click each section below to expand.
Any questions can be directed to the organizer, Jiyong Park (jiyong.park@uga.edu).