The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Multivariate Matching Methods That Are Monotonic Imbalance Bounding
Open accessAbstract
We introduce a new “Monotonic Imbalance Bounding” (MIB) class of matching methods for causal inference with a surprisingly large number of attractive statistical properties. MIB generalizes and extends in several new directions the only existing class, “Equal Percent Bias Reducing” (EPBR), which is designed to satisfy weaker properties and only in expectation. We also offer strategies to obtain specific members of the MIB class, and analyze in more detail a member of this class, called Coarsened Exact Matching, whose properties we analyze from this new perspective. We offer a variety of analytical results and numerical simulations that demonstrate how members of the MIB class can dramatically improve inferences relative to EPBR-based matching methods.
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Citations
Cites
The Effectiveness of Adjustment by Subclassification in Removing Bias in Observational Studies
Cochran, W. G. (1968) · Biometrics · doi:10.2307/2528036 · Cited by 961
Comparison of Multivariate Matching Methods: Structures, Distances, and Algorithms
Gu, X. S., & Rosenbaum, P. R. (1993) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.1993.10474623 · Cited by 702 · cited 2×
“We begin by replicating an experiment proposed Gu and Rosenbaum (1993).”
Method
Cited by
Causal Inference without Balance Checking: Coarsened Exact Matching
Iacus, S. M., King, G., & Porro, G. (2012) · Political Analysis · doi:10.1093/pan/mpr013 · Cited by 3,563
Self-citationWhy Propensity Scores Should Not Be Used for Matching
King, G., & Nielsen, R. (2019) · Political Analysis · doi:10.1017/pan.2019.11 · Cited by 1,706
Self-citationCovariate Balancing Propensity Score
Imai, K., & Ratkovic, M. (2014) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/rssb.12027 · Cited by 1,315
Matching Methods for Causal Inference with Time‐Series Cross‐Sectional Data
Imai, K., Kim, I. S., & Wang, E. H. (2023) · American Journal of Political Science · doi:10.1111/ajps.12685 · Cited by 349
Berkowitz, S. A., Terranova, J., Hill, C., Ajayi, T., Linsky, T., Tishler, L. W., & DeWalt, D. A. (2018) · Health Affairs · doi:10.1377/hlthaff.2017.0999 · Cited by 222
Metadata
- Authors
- Iacus, S. M., King, G., & Porro, G.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 2011-03
- Volume
- 106
- Issue
- 493
- Pages
- 345-361
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W2028442196
- Cited by
- 967
- References
- 2
- FWCI
- 29.77
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Iacus, S. M., King, G., & Porro, G. (2011). Multivariate Matching Methods That Are Monotonic Imbalance Bounding. Journal of the American Statistical Association. https://doi.org/10.1198/jasa.2011.tm09599
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