Causal inference and discovery in Python: (Record no. 5960)

MARC details
000 -LEADER
fixed length control field 02029nam a22002417a 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240210164507.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
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020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781804612989
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 005.133
Item number MOL
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Molak, Aleksander
245 ## - TITLE STATEMENT
Title Causal inference and discovery in Python:
Remainder of title unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Name of publisher, distributor, etc. Packt Publishing Ltd.
Place of publication, distribution, etc. Birmingham
Date of publication, distribution, etc. 2023
300 ## - PHYSICAL DESCRIPTION
Extent xxv, 423 p.
365 ## - TRADE PRICE
Price type code INR
Price amount 3699.00
520 ## - SUMMARY, ETC.
Summary, etc. Causal methods present unique challenges compared to traditional machine learning and statistics. Learning causality can be challenging, but it offers distinct advantages that elude a purely statistical mindset. Causal Inference and Discovery in Python helps you unlock the potential of causality. You’ll start with basic motivations behind causal thinking and a comprehensive introduction to Pearlian causal concepts, such as structural causal models, interventions, counterfactuals, and more. Each concept is accompanied by a theoretical explanation and a set of practical exercises with Python code. Next, you’ll dive into the world of causal effect estimation, consistently progressing towards modern machine learning methods. Step-by-step, you’ll discover Python causal ecosystem and harness the power of cutting-edge algorithms. You’ll further explore the mechanics of how “causes leave traces” and compare the main families of causal discovery algorithms. The final chapter gives you a broad outlook into the future of causal AI where we examine challenges and opportunities and provide you with a comprehensive list of resources to learn more.<br/><br/>(https://www.packtpub.com/product/causal-inference-and-discovery-in-python/9781804612989)
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computer science
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computer programming
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Programming languages
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Python
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Machine Learning
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Jaokar, Ajit
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Book
Source of classification or shelving scheme Dewey Decimal Classification
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Bill No Bill Date Home library Current library Shelving location Date acquired Source of acquisition Cost, normal purchase price Total Checkouts Full call number Accession Number Date last seen Copy number Cost, replacement price Price effective from Koha item type
    Dewey Decimal Classification     IT & Decisions Sciences TB3444 24-01-2024 Indian Institute of Management LRC Indian Institute of Management LRC General Stacks 02/10/2024 Technical Bureau India Pvt. Ltd. 2570.80   005.133 MOL 005749 02/10/2024 1 3699.00 02/10/2024 Book

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