Observation and Experiment: An Introduction to Causal Inference
Abstract
In the face of conflicting claims about some treatments, behaviors, and policies, the question arises: What is the most scientifically rigorous way to draw conclusions about cause and effect in the study of humans? In this introduction to causal inference, Paul Rosenbaum explains key concepts and methods through real-world examples.
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Such designs can account for differences between treatment and control groups that are not recorded in the data, which allows them to credibly identify the causal impact of coaching programmes on pupil learning.
“Such designs are able to account for differences between the treatment and control groups that are not recorded in the data, which enables them to credibly identify the causal impact of the coaching programmes on pupil learning (Rosenbaum, 2017).”
From Identifying the characteristics of effective teacher professional development: a critical review (2021) · cites this paper as Rosenbaum, 2017
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Cited by 170
- Authors
- Rosenbaum, P.
- Corresponding author
- Rosenbaum, Paul
- ORCID
- Rosenbaum, Paul
- Venue
- Harvard University Press eBooks
- Publisher
- Harvard University Press
- Published
- 2017-01-01
- OpenAlex
- W4300778175
- Cited by
- 170
- References
- 0
- Citations by year
- 2026: 10 · 2025: 20 · 2024: 11 · 2023: 16 · 2022: 12 · 2021: 18 · 2020: 57 · 2019: 19 · 2018: 7
- FWCI
- 5.88
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Harvard University Press eBooks (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Philosophy and History of Science
- Social Sciences › Decision Sciences › Management Science and Operations Research › Evaluation and Performance Assessment
- Keywords
- causal inference, causal effect estimation, randomized experiments, observational studies, confounding, treatment evaluation, human subjects research
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