arXiv:2403.02467
공식 코드 · Papers with Code
Applied Causal Inference Powered by ML and AI
원문 초록
An introduction to the emerging fusion of machine learning and causal inference. The book presents ideas from classical structural equation models (SEMs) and their modern AI equivalent, directed acyclical graphs (DAGs) and structural causal models (SCMs), and covers Double/Debiased Machine Learning methods to do inference in such models using modern predictive tools.
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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
Causal Effect Identification in lvLiNGAM from Higher-Order Cumulants
N$^2$: A Unified Python Package and Test Bench for Nearest Neighbor-Based Matrix Completion
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