Titlebook: Machine Learning for Causal Inference; Sheng Li,Zhixuan Chu Book 2023 The Editor(s) (if applicable) and The Author(s), under exclusive lic

NAUT

Causal Inference on Graphsct fields, such as social network analysis, bioinformatics, crime forecasting, economics, and recommender systems. Different from most traditional causal inference studies, which focus on independent and identically distributed (i.i.d.) data, causal inference on graphs has recently attracted increas

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Fair Machine Learning Through the Lens of Causality09) Causality. Cambridge University Press), this framework defines fairness in the categories of direct/indirect discrimination, system/group/individual-level discrimination, and their derivatives, e.g., indirect individual-level discrimination. The framework can unify various causal fairness notion

危险

Causal Explainable AIance measurements such as accuracy. However, as machine learning techniques have been applied to fields that are highly sensitive to risk, such as healthcare, law enforcement, and finance, the trustworthiness of models, especially their explainability, has become an increasingly important concern. F

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Causal Domain Generalization. assumption, independent and identically distributed assumption, states that the training and test data are sampled from the same distribution. On the other hand, real-world scenarios are more dynamic, with training and test data not always coming from the same distribution. In such cases, models b

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Causal Inference and Natural Language Processingl questions: (1) how can NLP aid in causal inference when working with textual data, and (2) how can causal inference theory enhance the robustness and interpretability of NLP models? We present the latest developments and challenges in each area. Firstly, we discuss the difficulties associated with

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