000			02618 a2200265 4500
003			OSt
005			20250422160150.0
008			220121b xxu||||| |||| 00| 0 eng d
020			_a9780367705053
040			_cIIT Kanpur
041			_aeng
082			_a519.50285 _bB834f
100			_aBrumback, Babette A.
245			_aFundamentals of causal inference _bwith R _cBabette A. Brumback
260			_bCRC Press _c2022 _aBoca Raton, FL
300			_axii,236p
440			_aChapman and Hall/CRC texts in statistical science series
490			_a /edited by Joseph K. Blitzstein ...[et al.]
500			_aIncludes bibliographical references and index
520			_aOne 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. Fundamentals of Causal Inference 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 examples, simulation studies, and analyses using R motivate the methods throughout. The book assumes familiarity with basic statistics and probability, regression, and R and is suitable for seniors or graduate students in statistics, biostatistics, and data science as well as PhD students in a wide variety of other disciplines, including epidemiology, pharmacy, the health sciences, education, and the social, economic, and behavioral sciences. Beginning with a brief history and a review of essential elements of probability and statistics, a unique feature of the book is its focus on real and simulated datasets with all binary variables to reduce complex methods down to their fundamentals. Calculus is not required, but a willingness to tackle mathematical notation, difficult concepts, and intricate logical arguments is essential. While many real data examples are included, the book also features the Double What-If Study, based on simulated data with known causal mechanisms, in the belief that the methods are best understood in circumstances where they are known to either succeed or fail. Datasets, R code, and solutions to odd-numbered exercises are available at www.routledge.com.
650			_aMathematical statistics -- Data processing
650			_aCausation
650			_aInference
942			_cBK
999			_c565213 _d565213