Ubiquitous Laplacian An Introduction To Numerical Pdes With Applications In Data Science | 30.22 MB
Title: Ubiquitous Laplacian: An Introduction to Numerical PDEs With Applications in Data Science (307 Pages)
Author: Rongjie Lai & Xiangxiong Zhang
Category: Nonfiction, Social & Cultural Studies, Social Science, Statistics, Methodology, Reference & Language, Reference
Language: English | 308 Pages | ISBN: 9819814537
Description:
Understanding cause-and-effect relationships is essential for credible research and informed decision-making. The Data Analyst’s Guide to Cause and Effect offers a clear, practical roadmap for answering causal questions using both experimental and observational data.
Built around the EEESI workflow-Estimand, Estimator, Estimate, Simulation-based Inference-this book provides a systematic approach to defining, estimating, and validating causal effects. Readers will learn to apply modern techniques such as g-methods, inverse probability weighting, poststratification, and multilevel modeling, while tackling challenges like confounding and missing data.
With hands-on examples in R, code snippets, and simulation exercises, this guide balances rigor with accessibility. Ideal for graduate courses and applied researchers, it equips readers to move beyond simple associations and make credible causal inferences that inform theory, policy, and practice.
Understanding cause-and-effect relationships is essential for credible research and informed decision-making. The Data Analyst’s Guide to Cause and Effect offers a clear, practical roadmap for answering causal questions using both experimental and observational data.
Built around the EEESI workflow-Estimand, Estimator, Estimate, Simulation-based Inference-this book provides a systematic approach to defining, estimating, and validating causal effects. Readers will learn to apply modern techniques such as g-methods, inverse probability weighting, poststratification, and multilevel modeling, while tackling challenges like confounding and missing data.
With hands-on examples in R, code snippets, and simulation exercises, this guide balances rigor with accessibility. Ideal for graduate courses and applied researchers, it equips readers to move beyond simple associations and make credible causal inferences that inform theory, policy, and practice.
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