arXiv:2405.08793
공식 코드 · Papers with Code
A Brief Introduction to Causal Inference in Machine Learning
원문 초록
This is a lecture note produced for DS-GA 3001.003 "Special Topics in DS - Causal Inference in Machine Learning" at the Center for Data Science, New York University in Spring, 2024. This course was created to target master's and PhD level students with basic background in machine learning but who were not exposed to causal inference or causal reasoning in general previously. In particular, this course focuses on introducing such students to expand their view and knowledge of machine learning to incorporate causal reasoning, as this aspect is at the core of so-called out-of-distribution generalization (or lack thereof.)
함께 살펴볼 논문
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On the Out-of-Distribution Generalization of Self-Supervised Learning
Learning Causally Predictable Outcomes from Psychiatric Longitudinal Data
Towards Robust Multimodal Emotion Recognition under Missing Modalities and Distribution Shifts
From Images to Insights: Explainable Biodiversity Monitoring with Plain Language Habitat Explanations
Paths to Causality: Finding Informative Subgraphs Within Knowledge Graphs for Knowledge-Based Causal Discovery
Positional Encoding meets Persistent Homology on Graphs
토론
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