Durai Rajamanickam: Causal Inference for Machine Learning Engineers, Kartoniert / Broschiert

Klappentext

.- 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.