Causal Inference in Statistics, Social, and Biomedical Sciences
Causal Inference in Statistics, Social, and Biomedical Sciences Most questions in social and biomedical sciences are causal in nature: what would happen to individuals, or to groups, if part of their…
Specifikacia Causal Inference in Statistics, Social, and Biomedical Sciences
Causal Inference in Statistics, Social, and Biomedical Sciences
Most questions in social and biomedical sciences are causal in nature: what would happen to individuals, or to groups, if part of their environment were changed? This book starts with the notion of potential outcomes, each corresponding to the outcome that would be realized if a subject were exposed to a particular treatment or regime. In this groundbreaking text, two world-renowned experts present statistical methods for studying such questions.
The fundamental problem of causal inference is that we can only observe one of the potential outcomes for a particular subject. In this approach, causal effects are comparisons of such potential outcomes. The authors discuss how randomized experiments allow us to assess causal effects and then turn to observational studies.
They lay out the assumptions needed for causal inference and describe the leading analysis methods, including matching, propensity-score methods, and instrumental variables. Many detailed applications are included, with special focus on practical aspects for the empirical researcher.
Explanation in Causal Inference
Causal Inference
Fundamentals of Causal Inference
Causal Inference: The Mixtape
ENGLISH FOR BIOMEDICAL SCIENCES