[Open DMQA Seminar] Causal Inference

Автор: ‍김성범[ 교수 / 산업경영공학부 ]

Загружено: 2023-04-09

Просмотров: 6010

Описание: 인과추론(causal inference)은 원인과 결과 사이의 인과관계를 추론하는 과정이다. 일반적인 인공지능 알고리즘은 데이터의 상관관계를 파악(pattern recognition)하여 분류, 예측 등의 문제를 해결한다. 하지만 상관관계는 인과관계를 의미하지 않는다. 만약 인과관계를 추론할 수 있다면, 관찰된 상황과 다른 환경에서도 robust한 예측이 가능하고 복잡한 학습 모델의 특성을 이해할 수 있다. 본 세미나에서는 인과추론에 대한 개념 설명과 함께 ML/DL 알고리즘을 기반으로 인과관계를 추정하는 방법론을 소개한다.

참고문헌:
[1] Künzel, Sören R., et al. "Metalearners for estimating heterogeneous treatment effects using machine learning." Proceedings of the national academy of sciences 116.10 (2019): 4156-4165.
[2] Shalit, Uri, Fredrik D. Johansson, and David Sontag. "Estimating individual treatment effect: generalization bounds and algorithms." International Conference on Machine Learning. PMLR, 2017.
[3] Shi, Claudia, David Blei, and Victor Veitch. "Adapting neural networks for the estimation of treatment effects." Advances in neural information processing systems 32 (2019).

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