The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Matching Estimators of Causal Effects: Prospects and Pitfalls in Theory and Practice
Abstract
As the counterfactual model of causality has increased in popularity, sociologists have returned to matching as a research methodology. In this article, advances over the past two decades in matching estimators are explained, and the practical limitations of matching techniques are emphasized. The authors introduce matching methods by focusing first on ideal scenarios in which stratification and weighting procedures warrant causal inference. Then, they discuss how matching is often undertaken in practice, offering an overview of the most prominent data analysis routines. With four hypothetical examples, they demonstrate how the assumptions behind matching estimators often break down in practice. Even so, the authors argue that matching techniques can be used effectively to strengthen the prosecution of causal questions in sociology.
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Cites
Causality: Models, Reasoning, and Inference
Pearl, J. (2009) · doi:10.1017/cbo9780511803161 · Cited by 11,653
Statistics and Causal Inference
Holland, P. W. (1986) · Journal of the American Statistical Association · doi:10.1080/01621459.1986.10478354 · Cited by 5,293
Does matching overcome lalonde’s critique of nonexperimental estimators?
A. Smith, J., & E. Todd, P. (2005) · Journal of Econometrics · doi:10.1016/j.jeconom.2004.04.011 · Cited by 3,176
Characterizing Selection Bias Using Experimental Data
Heckman, J., Ichimura, H., Smith, J., & Todd, P. (1998) · Econometrica · doi:10.2307/2999630 · Cited by 2,286
Empirical Strategies in Labor Economics
Angrist, J. D., & Krueger, A. B. (1999) · Handbook of Labor Economics · doi:10.1016/s1573-4463(99)03004-7 · Cited by 1,628
The Effectiveness of Adjustment by Subclassification in Removing Bias in Observational Studies
Cochran, W. G. (1968) · Biometrics · doi:10.2307/2528036 · Cited by 961
THE ESTIMATION OF CAUSAL EFFECTS FROM OBSERVATIONAL DATA
Winship, C., & Morgan, S. L. (1999) · Annual Review of Sociology · doi:10.1146/annurev.soc.25.1.659 · Cited by 890
Self-citationMatching to remove bias in observational studies
Rubin, D. B. (1973) · Biometrics · doi:10.2307/2529684 · Cited by 737
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Matching Methods for Causal Inference: A Review and a Look Forward
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Case Selection Techniques in Case Study Research: A Menu of Qualitative and Quantitative Options
Seawright, J., & Gerring, J. (2008) · Political Research Quarterly · doi:10.1177/1065912907313077 · Cited by 3,827
Causal inference with observational data
Nichols, A. (2007) · The Stata Journal: Promoting communications on statistics and Stata · doi:10.1177/1536867x0800700403 · Cited by 339
(2011) · The University of Bath Online Publications Store (The University of Bath) · Cited by 284
The impact of entrepreneurship education in high school on long-term entrepreneurial performance
Elert, N., Andersson, F. W., & Wennberg, K. (2015) · Journal of Economic Behavior & Organization · doi:10.1016/j.jebo.2014.12.020 · Cited by 290
Kim, R. H., & Clark, D. (2013) · Journal of Criminal Justice · doi:10.1016/j.jcrimjus.2013.03.001 · Cited by 122
Reynolds, C. L., & DesJardins, S. L. (2009) · Higher Education: Handbook of Theory and Research · doi:10.1007/978-1-4020-9628-0_2 · Cited by 104
Liminal Men: Incarceration and Relationship Dissolution
Turney, K. (2015) · Social Problems · doi:10.1093/socpro/spv015 · Cited by 103
Eisner, M., Nagin, D., Ribeaud, D., & Malti, T. (2012) · Prevention Science · doi:10.1007/s11121-011-0266-x · Cited by 89
Gaddis, S. M. (2012) · Social Forces · doi:10.1093/sf/sos003 · Cited by 84
Kot, F. C., & Jones, J. L. (2015) · College & Research Libraries · doi:10.5860/crl.76.5.566 · Cited by 47
Metadata
- Authors
- Morgan, S. L., & Harding, D. J.
- Venue
- Sociological Methods & Research
- Publisher
- SAGE Publications
- Published
- 2006-08
- Volume
- 35
- Issue
- 1
- Pages
- 3-60
- ISSN
- 0049-1241 (ISSN-L)
- 1552-8294
- OpenAlex
- W2055494407
- Cited by
- 392
- References
- 8
- FWCI
- 12.81
- Type
- journal article
- Language
- en
- Open access
- closed
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