Focusing on statistical inference, this book offers an introduction to the methodologies used in observational studies, emphasizing their importance in understanding treatment effects on individuals. It also delves into the principles that guide the design of these studies, making it a valuable resource for researchers and practitioners seeking to enhance their understanding of observational research.
A nontechnical guide to the basic ideas of modern causal inference, with illustrations from health, the economy, and public policy.Which of two antiviral drugs does the most to save people infected with Ebola virus? Does a daily glass of wine prolong or shorten life? Does winning the lottery make you more or less likely to go bankrupt? How do you identify genes that cause disease? Do unions raise wages? Do some antibiotics have lethal side effects? Does the Earned Income Tax Credit help people enter the workforce?Causal Inference provides a brief and nontechnical introduction to randomized experiments, propensity scores, natural experiments, instrumental variables, sensitivity analysis, and quasi-experimental devices. Ideas are illustrated with examples from medicine, epidemiology, economics and business, the social sciences, and public policy.
We hear that a glass of red wine prolongs life, that alcohol is a carcinogen, that pregnant women should drink not a drop of alcohol. Major medical journals first claimed that hormone replacement therapy reduces the risk of heart disease, then reversed themselves and said it increases the risk of heart disease. What are the effects caused by consuming alcohol or by receiving hormone replacement therapy? These are causal questions, questions about the effects caused by treatments, policies or preventable exposures. Some causal questions can be studied in randomized trials, in which a coin is flipped to decide the treatment for the next experimental subject. Because randomized trials are not always practical, nor always ethical, many causal questions are investigated in non-randomized observational studies. The reversal of opinion about hormone replacement therapy occurred when a randomized clinical trial contradicted a series of earlier observational studies. Using minimal mathematics--high school algebra and coin flips--and numerous examples, Observation and Experiment explains the key concepts and methods of causal inference. Examples of randomized experiments and observational studies are drawn from clinical medicine, economics, public health and epidemiology, clinical psychology and psychiatry.-- Provided by publisher