Cited by 4,210Advanced Causal Inference TechniquesStatistical Methods and Bayesian InferencePhilosophy of Biology and Scientific Explanation

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Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction

Imbens, Guido W., Rubin, Donald B.·2015·Cambridge University Press eBooks

DOI 10.1017/cbo9781139025751

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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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Authors: Imbens, G. W., & Rubin, D. B.
Affiliations: Stanford University (US)
Harvard University (US)
ORCID: Imbens, Guido W.
Rubin, Donald B.
Venue: Cambridge University Press eBooks
Publisher: Cambridge University Press
Published: 2015-04-06
DOI: 10.1017/cbo9781139025751
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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Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction

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