Foundations of Agnostic Statistics

Reflecting a sea change in how empirical research has been conducted over the past three decades, Foundations of Agnostic Statistics presents an innovative treatment of modern statistical theory for the social and health sciences. This book develops the fundamentals of what the authors call agnostic statistics, which considers what can be learned about the world without assuming that there exists a simple generative model that can be known to be true. Aronow and Miller provide the foundations for statistical inference for researchers unwilling to make assumptions beyond what they or their audience would find credible. Building from first principles, the book covers topics including estimation theory, regression, maximum likelihood, missing data, and causal inference. Using these principles, readers will be able to formally articulate their targets of inquiry, distinguish substantive assumptions from statistical assumptions, and ultimately engage in cutting-edge quantitative empirical research that contributes to human knowledge.
Provides a rigorous and targeted mathematical introduction to the statistics underlying modern statistical methodology in the social and health sciences
Prepares readers to go on to advanced study in statistical methodology, including in causal inference, nonparametric statistics, and econometrics
Develops the fundamentals of 'agnostic statistics' - an approach that asks what can be learned about the world under minimal assumptions