Association does not imply causation, yet some causal conclusions are firmly established based on associations found in observational studies. How does that happen? A study has two evidence factors if it provides two statistically independent tests of one causal hypothesis, susceptible to different biases. Two evidence factors can jointly provide quantifiably stronger evidence than either factor can provide on its own. The first book about evidence factors. Examples are drawn from epidemiology, economics, medical research and other fields. Data from these examples is available in a companion R package that reproduces many of the analyses. Self-contained, presenting needed background from causal inference, statistics and mathematics. Part 1 of the book presents concepts, methods and applications using limited mathematics. The theory of evidence factors is presented in a separate, second part of the book. Mathematics required for the theory is presented from the beginning.

Association does not imply causation, yet some causal conclusions are firmly established based on associations found in observational studies. How does that happen? A study has two evidence factors if it provides two statistically independent tests of one causal hypothesis, susceptible to different biases. Two evidence factors can jointly provide quantifiably stronger evidence than either factor can provide on its own. The first book about evidence factors. Examples are drawn from epidemiology, economics, medical research and other fields. Data from these examples is available in a companion R package that reproduces many of the analyses. Self-contained, presenting needed background from causal inference, statistics and mathematics. Part 1 of the book presents concepts, methods and applications using limited mathematics. The theory of evidence factors is presented in a separate, second part of the book. Mathematics required for the theory is presented from the beginning.

A daily glass of wine prolongs life-yet alcohol can cause life-threatening cancer. Some say raising the minimum wage will decrease inequality while others say it increases unemployment. Scientists once confidently claimed that hormone replacement therapy reduced the risk of heart disease but now they equally confidently claim it raises that risk. What should we make of this endless barrage of conflicting claims? Observation and Experiment is an introduction to causal inference by one of the field's leading scholars. An award-winning professor at Wharton, Paul Rosenbaum explains key concepts and methods through lively examples that make abstract principles accessible. He draws his examples from clinical medicine, economics, public health, epidemiology, clinical psychology, and psychiatry to explain how randomized control trials are conceived and designed, how they differ from observational studies, and what techniques are available to mitigate their bias. "Carefully and precisely written...reflecting superb statistical understanding, all communicated with the skill of a master teacher." -Stephen M. Stigler, author of The Seven Pillars of Statistical Wisdom "An excellent introduction...Well-written and thoughtful...from one of causal inference's noted experts." -Journal of the American Statistical Association "Rosenbaum is a gifted expositor...an outstanding introduction to the topic for anyone who is interested in understanding the basic ideas and approaches to causal inference." -Psychometrika "A very valuable contribution...Highly recommended." -International Statistical Review