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.- 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.
.- 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.
Durai Rajamanickam is a distinguished AI and data science leader with over two decades of experience, specializing in the application of machine learning to critical real-world challenges in healthcare, finance, and legal technology. Renowned for his ability to distill complex theoretical concepts into actionable solutions, he has spearheaded transformative AI initiatives across various industries.
.- 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.
| Erscheinungsjahr: | 2026 |
|---|---|
| Genre: | Informatik, Mathematik, Medizin, Naturwissenschaften, Technik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Inhalt: |
xxi
245 S. 28 s/w Illustr. 25 farbige Illustr. 245 p. 53 illus. 25 illus. in color. |
| ISBN-13: | 9783031996795 |
| ISBN-10: | 3031996798 |
| Sprache: | Englisch |
| Einband: | Kartoniert / Broschiert |
| Autor: | Rajamanickam, Durai |
| Hersteller: |
Springer
Springer International Publishing AG |
| Verantwortliche Person für die EU: | Springer Verlag GmbH, Tiergartenstr. 17, D-69121 Heidelberg, juergen.hartmann@springer.com |
| Maße: | 235 x 155 x 15 mm |
| Von/Mit: | Durai Rajamanickam |
| Erscheinungsdatum: | 03.01.2026 |
| Gewicht: | 0,411 kg |
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