<p><i>"Overall, this textbook is a perfect guide for interested researchers and students who wish to understand the rationale and methods of causal inference. Each chapter provides an R implementation of the introduced causal concepts and models and concludes with appropriate exercises."<br/></i>-An-Shun Tai & Sheng-Hsuan Lin, in <i>Biometrics</i><br/><br/>One of the primary motivations for clinical trials and observational studies of humans is to infer cause and effect. Disentangling causation from confounding is of utmost importance. <b>Fundamentals of Causal Inference</b> explains and relates different methods of confounding adjustment in terms of potential outcomes and graphical models, including standardization, difference-in-differences estimation, the front-door method, instrumental variables estimation, and propensity score methods. It also covers effect-measure modification, precision variables, mediation analyses, and time-dependent confounding. Several real data examp
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<b>A nontechnical guide to the basic ideas of modern causal inference, with illustrations from health, the economy, and public policy.</b><br><br>Which of two antiviral drugs does the most to save people infected with Ebola virus?...
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An accessible and contemporary introduction to the methods for determining cause and effect in the social sciences
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In this book, author Matheus Facure explains the untapped potential of causal inference for estimating impacts and effects.
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