Statistical Models and Causal Inference
A Dialogue with the Social Sciences
Bibliographic Data
| ID | 19902999 |
|---|---|
| Authors | David A Freedman (corresponding author) |
| Editors | David Collier, Jasjeet S Sekhon (0009-0003-1218-0117), Philip B Stark (0000-0002-3771-9604) |
| Year | 2009 |
| Publication date | 2009-11-23 |
| Open Access | Yes |
| Type | BOOK |
| Venue | Statistical Models and Causal Inference (SOURCE_BOOK) |
| Publisher | Cambridge University Press (PUBLISHER • US) |
| DOI | 10.1017/cbo9780511815874 |
| OpenAlex | W25574983 |
| Open Library | OL23910135M |
| ISBN | 9780511815874 |
| Language | EN |
| Citations received | 34 |
David A. Freedman presents here a definitive synthesis of his approach to causal inference in the social sciences. He explores the foundations and limitations of statistical modeling, illustrating basic arguments with examples from political science, public policy, law, and epidemiology. Freedman maintains that many new technical approaches to statistical modeling constitute not progress, but regress. Instead, he advocates a 'shoe leather' methodology, which exploits natural variation to mitigate confounding and relies on intimate knowledge of the subject matter to develop meticulous research designs and eliminate rival explanations. When Freedman first enunciated this position, he was met with scepticism, in part because it was hard to believe that a mathematical statistician of his stature would favor 'low-tech' approaches. But the tide is turning. Many social scientists now agree that statistical technique cannot substitute for good research design and subject matter knowledge. This book offers an integrated presentation of Freedman's views
Causal inference · Data science · Econometrics · Economics · Epistemology · Exploit · Freedman · Inference · Management science · Political science · Presentation (obstetrics) · Skepticism · Statistical inference · Statistician · Statistics · Subject (documents) · Artificial Intelligence · Computer Science · Engineering · Law · Mathematics · Philosophy · Qualitative Comparative Analysis Research
Expanding the History and Range of Mixed Methods Research
A Realist Alternative to Randomised Control Trials
Hard and Soft Obscurantism in the Humanities and Social Sciences
On the Concept of Power
Causality in Policy Studies
Making Young Voters
Power and Political Theory
How do social scientists reach causal inferences? A study of reception
Divine Direction
Nonparametric Identification of Causal Effects under Temporal Dependence
Why Causal Mechanisms and Process Tracing Should Alter Case Selection Guidance
Concepts, Measures, and Measuring Well
Natural Experiments and Pluralism in Political Science
Tool-box or toy-box? Hard obscurantism in economic modeling
Structural determinants of human rights prosecutions after democratic transition
Seven deadly sins of contemporary quantitative political analysis
Øvst ved bordet?»
Obscurantisme dur et obscurantisme mou dans les sciences humaines et sociales
Thinking about the Role of Religion in Foreign Policy
Regression Analysis and Causal Inference
Postadoption and Guardianship
From Effects of Governance to Causes of Epistemic Change
Description, Causal Explanation, and Policy Intervention in Sociology
Does poverty reduce mental health? An instrumental variable analysis
Understanding Process Tracing
Aggregating Political Dimensions
The Importance of Qualitative Research for Causal Explanation in Education
Flexible Causal Inference for Political Science
A New Case for the Study of Individual Events in Political Science
Nonparametric Combination (NPC)
From Methodology to Practice
Set-Theoretic Comparative Methods
Causal inference in political science research
The Development of Students’ Engagement in School, Community and Democracy
| Unique citing works | 34 |
|---|---|
| Citations per year | 2,13 |
| Citation span | 2010 - 2025 (16) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 31 |