Baskadia

Machine Learning for Causal Inference by Sheng Li, Zhixuan Chu

• Machine Learning for Causal Inference
  

• Sheng Li, Zhixuan Chu
  

• Page: 298
  

• Format: pdf, ePub, mobi, fb2
  

• ISBN: 9783031350504
  

• Publisher: Springer International Publishing
  

Download Machine Learning for Causal Inference

Amazon kindle audio books download Machine Learning for Causal Inference (English Edition)

This book provides a deep understanding of the relationship between machine learning and causal inference. It covers a broad range of topics, starting with the preliminary foundations of causal inference, which include basic definitions, illustrative examples, and assumptions. It then delves into the different types of classical causal inference methods, such as matching, weighting, tree-based models, and more. Additionally, the book explores how machine learning can be used for causal effect estimation based on representation learning and graph learning. The contribution of causal inference in creating trustworthy machine learning systems to accomplish diversity, non-discrimination and fairness, transparency and explainability, generalization and robustness, and more is also discussed. The book also provides practical applications of causal inference in various domains such as natural language processing, recommender systems, computer vision, time series forecasting, and continual learning. Each chapter of the book is written by leading researchers in their respective fields. Machine Learning for Causal Inference explores the challenges associated with the relationship between machine learning and causal inference, such as biased estimates of causal effects, untrustworthy models, and complicated applications in other artificial intelligence domains. However, it also presents potential solutions to these issues. The book is a valuable resource for researchers, teachers, practitioners, and students interested in these fields. It provides insights into how combining machine learning and causal inference can improve the system's capability to accomplish causal artificial intelligence based on data. The book showcases promising research directions and emphasizes the importance of understanding the causal relationship to construct different machine-learning models from data.

Elements of Causal Inference: Foundations and Learning
A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning. The mathematization of causality 
Machine Learning and Causal Inference
In adition, we included tutorials on Heterogenous Treatment Effects Using Causal Trees and Causal Forest from Susan Athey's Machine Learning and Causal 
What are the new developments in causal inference for ML?
Oct 24, 2023 —
Machine Learning for Causal Inference (Hardcover)
Machine Learning for Causal Inference explores the challenges associated with the relationship between machine learning and causal inference, such as biased 
Causal Inference with Machine Learning August 25, 2022
by V Chernozhukov · 2022 —
Building New Tools at the Intersection of Statistical Machine
Apr 14, 2022 —
Causal Inference and Machine Learning
Causal Inference and Machine Learning · The algorithm for detecting higher variable causal graphs is slow, since the number of graphs grows super-exponentially 
Chapter 9 Additional Resources | Machine Learning-based
9.1 Machine Learning & Causal Inference: An Introductory Course · a high-level overview contrasting traditional econometrics with off-the-shelf machine learning 
Causal Machine Learning: A Survey and Open Problems
by J Kaddour · 2022 · Cited by 66 —
Machine Learning & Causal Inference: A Short Course
Machine Learning & Causal Inference: A Short Course This course is a series of videos designed for any audience looking to learn more about