Causal Inference in Statistics: A Primer
by Judea Pearl, Madelyn Glymour, Nicholas P. Jewell
Causal Inference in Statistics: A Primer by Judea Pearl, Madelyn Glymour, and Nicholas P. Jewell offers a concise introduction to causal reasoning in statistics. It explores methods for identifying and estimating causal effects, using graphical models and counterfactuals to help readers distinguish causation from correlation in data analysis. Ideal for students and researchers seeking foundational knowledge.
About This Book
Causal Inference in Statistics: A Primer provides an accessible introduction to the principles of causal inference within a statistical framework. It equips readers with essential tools to move beyond correlation and toward understanding causation in data analysis.
Written by Judea Pearl, Madelyn Glymour, and Nicholas P. Jewell, the book emphasizes practical applications of causal models, making complex concepts approachable for those new to the field.
Through clear explanations and examples, it covers key topics such as counterfactuals, graphical models, and estimation techniques, serving as a valuable resource for statisticians, social scientists, and epidemiologists.
The primer highlights the importance of causal thinking in modern data-driven decision-making, encouraging rigorous approaches to interpreting observational and experimental data.
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