The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction
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 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.
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“Under Assumption 1, the standard results for the variance of the difference in means for a randomized experiments apply (e.g., Neyman [1923/1990], Imbens and Rubin [2015]), and the exact variance of ˆ τ did is,”
From Design-based analysis in Difference-In-Differences settings with staggered adoption (2022) · cites this paper as Imbens and Rubin [2015]
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VanderWeele, T. J., & Ding, P. (2017) · Annals of Internal Medicine · doi:10.7326/m16-2607 · Cited by 6,350
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Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W. K., & Robins, J. M. (2017) · Econometrics Journal · doi:10.1111/ectj.12097 · Cited by 3,595
Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
Wager, S., & Athey, S. (2018) · Journal of the American Statistical Association · doi:10.1080/01621459.2017.1319839 · Cited by 2,917
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Yu, B., & Kumbier, K. (2018) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.1700813 · Cited by 2,367
Definitions, methods, and applications in interpretable machine learning
Murdoch, W. J., Singh, C., Kumbier, K., Abbasi-Asl, R., & Yu, B. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1900654116 · Cited by 2,167
Metadata
- Authors
- Imbens, G. W., & Rubin, D. B.
- Affiliations
- Stanford University (US)
- Harvard University (US)
- Venue
- Cambridge University Press eBooks
- Publisher
- Cambridge University Press
- Published
- 2015-04-06
- OpenAlex
- W2263423262
- Cited by
- 4,210
- References
- 0
- Citations by year
- 2026: 415 · 2025: 533 · 2024: 521 · 2023: 470 · 2022: 466 · 2021: 476 · 2020: 395 · 2019: 346 · 2018: 238 · 2017: 184 · 2016: 119 · 2015: 30
- FWCI
- 47.73
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Cambridge University Press eBooks (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Philosophy of Biology and Scientific Explanation
- Keywords
- causal inference, potential outcomes, causal effects, randomized experiments, observational studies, matching, propensity score methods, instrumental variables, causal assumptions, social sciences, biomedical sciences
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