Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction
Stanford University · Harvard University Press
Abstract
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? In this groundbreaking text, two world-renowned experts present statistical methods for studying such questions. 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 approach, causal effects are comparisons of such potential outcomes. The fundamental problem of causal inference is that we can only observe one of the potential outcomes for a particular subject. The authors discuss how randomized experiments allow us to…
Citation impact
- FWCI
- 19.58
- Percentile
- 100%
- References
- 268
Authors
2Topics & keywords
- Causal inference
- Propensity score matching
- Observational study
- Matching (statistics)
- Randomized experiment
- Statistical inference
- Subject (documents)
- Inference