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Causal Inference for Machine Learning Engineers

By Durai Rajamanickam

Causal Inference for Machine Learning Engineers

By Durai Rajamanickam

0 - Default Title
Description
.- Introduction to Causal Thinking..- Treatments, Outcomes, and Confounding: Core Concepts..- Causal Estimation Basics..- Causal Graphs: Structure and Assumptions..- Interventions and Counterfactuals..- Introduction to Do-Calculus..- Backdoor and Frontdoor Criteria..- Advanced Causal Inference Methods..- Causal Inference Meets Deep Learning..- Simulating Causal Data and Evaluation Met rics..- Balancing Representations with Causal Deep Learning (CFRNet)..- Propensity Scores in Causal Deep Learning..- Evaluating Causal Models Without Counter factuals..- Advanced Topics in Causal Inference..- Assumptions and Real-World Challenges in Causal Inference..- Summary of Key Concepts..- Case Studies..- Solutions to Exercises.
Product details
Binding:
Paperback
Number of Pages:
268
Release Date:
2026-01-03
Publication Date:
2026-01-03
Publisher:
Springer
Languages:
Original: English
ISBN10:
3031996798
ISBN13:
9783031996795
GPSR Manufacturer Reference:
[email protected]
Weight:
411 g
Height:
155 cm
Width:
235 cm
Thickness:
15 cm
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