Price loading...

Causal Deep Learning: Encouraging Impact on Real-world Problems Through Causality (Foundations and Trends® in Signal Processing)

AI & Machine Learning

Price data last checked 108 day(s) ago - refreshing...

View at Amazon

Price History & Forecast

No Price Data Available

Price history will appear here once data is collected from Amazon.

Price Distribution

No price data available for histogram

Description

Causality has the potential to truly transform the way we solve a large number of real-world problems. Yet, so far, its potential largely remains to be unlocked as causality often requires crucial assumptions which cannot be tested in practice. This monograph, entitled Causal Deep Learning (CDL), presents a new way of looking at causality. The causal deep learning framework in this monograph spans three dimensions: (1) a structural dimension, which incorporates partial yet testable causal knowledge rather than assuming either complete or no causal knowledge among the variables of interest; (2) a parametric dimension, which encompasses parametric forms that capture the type of relationships among the variables of interest; and (3) a temporal dimension, which captures exposure times or how the variables of interest interact (possibly causally) over time. The CDL framework used enables precise categorisation and comparison of causal statistical learning methods. This categorisation is used to provide a comprehensive review of the CDL field. More importantly, CDL enables progress on a variety of real-world problems by aiding partial causal knowledge (including independencies among variables) and quantitatively characterising causal relationships among variables of interest (possibly over time). The framework used clearly identifies which assumptions are testable and which are not, so the resulting solutions can be judiciously adopted in practice. This formulation helps to combine or chain causal representations to solve specific problems without losing track of which assumptions are required to build these solutions, pushing real-world impact in healthcare, economics and business, environmental sciences and education, through causal deep learning.

Product Specifications

Format: paperback
ASIN: 1638284008
Category: Books > Subjects > Computing & Internet > Computer Science > AI & Machine Learning
Domain: Amazon UK
Release Date: 01 August 2024
Listed Since: 05 August 2024

Barcode

No barcode data available

Causal Deep Learning: Encouraging Impact on Real-world Problems Through Causality (Foundations and Trends® in Signal Processing)

Price History & Forecast

No Price Data Available

Price Distribution

Description

Product Specifications

Barcode

Similar Products You Might Like

Model-Based Deep Learning (Foundations and Trends® in Signal Processing)

Causal Inference and Discovery in Python: Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more

Machine Learning for Causal Inference

Cause Effect Pairs in Machine Learning (The Springer Series on Challenges in Machine Learning)

Foundations of Deep Learning (Machine Learning: Foundations, Methodologies, and Applications)

Fundamentals and Methods of Machine and Deep Learning: Algorithms, Tools, and Applications

Deep Reinforcement Learning and Its Industrial Use Cases: AI for Real-World Applications

Springer Multi-faceted Deep Learning: Models and Data Book

Machine Learning Paradigms: Advances in Deep Learning-based Technological Applications: 18 (Learning and Analytics in Intelligent Systems, 18)

Academic Press Deep Learning for Data Analytics Textbook

Deep Learning for Multi-Sensor Earth Observation

Deep Learning Systems: Algorithms, Compilers, and Processors for Large-Scale Production (Synthesis Lectures on Computer Architecture)

Computational Analysis and Deep Learning for Medical Care: Principles, Methods, and Applications

Deep Learning Applications: In Computer Vision, Signals And Networks

Deep Learning for Medical Applications with Unique Data

Trends in Deep Learning Methodologies: Algorithms, Applications, and Systems (Hybrid Computational Intelligence for Pattern Analysis and Understanding)

Deep Learning for the Earth Sciences: A Comprehensive Approach to Remote Sensing, Climate Science and Geosciences

Springer Deep Learning in Healthcare: Paradigms and Applications

Penguin Random House Deep Learning (Adaptive Computation and Machine Learning series)

Deep Generative Modeling

Neural Networks and Deep Learning: A Textbook

Deep Learning for Physical Scientists: Accelerating Research with Machine Learning

Deep Learning For 3d Vision: Algorithms And Applications

Deep Learning for Sustainable Agriculture (Cognitive Data Science in Sustainable Computing)