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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

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Metadata

Record

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