The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Controlling Selection Bias in Causal Inference
Abstract
Selection bias, caused by preferential exclusion of units (or samples) from the data, is a major obstacle to valid causal inferences, for it cannot be removed or even detected by randomized experiments. This paper highlights several graphical and algebraic methods capable of mitigating and sometimes eliminating this bias. These nonparametric methods generalize and improve previously reported results, and identify the type of knowledge that need to be available for reasoning in the presence of selection bias
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Citations
Cites
A structural approach to selection bias
Hernán, M. A., Hernández-Díaz, S., & Robins, J. M. (2004) · Epidemiology · doi:10.1097/01.ede.0000135174.63482.43 · Cited by 2,844
Learning and evaluating classifiers under sample selection bias
Zadrozny, B. (2004) · Twenty-first international conference on Machine learning - ICML '04 · doi:10.1145/1015330.1015425 · Cited by 800
Data, Design, and Background Knowledge in Etiologic Inference
Robins, J. M. (2001) · Epidemiology · doi:10.1097/00001648-200105000-00011 · Cited by 358 · cited 2×
“, cases may be reported only when the outcome (disease or complication) is unusual, while non-cases remain unreported (see (Glymour and Greenland, 2008; Robins et al., 2000; Robins, 2001; Hernán et al., 2004)).”
Background
Research works printed but not linked: 6
- Cornfield, J. 1951. A method of estimating comparative rates from clinical data; applications to cancer of the lung, breast, and cervix. Journal of the National Cancer Institute 11:1269-1275.
- Geng, Z. 1992. Collapsibility of relative risk in contingency ta- bles with a response variable. Journal Royal Statistical Society 54(2):585-593.
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- Horwitz, R., and Feinstein, A. 1978. Alternative analytic methods for case-control studies of estrogens and endometrial cancer. New England Journal of Medicine 299:368-387.
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Cited by
Sorted by citations
Linear Models: A Useful "Microscope" for Causal Analysis
Pearl, J. (2013) · Journal of Causal Inference · doi:10.1515/jci-2013-0003 · Cited by 132
Self-citationPollet, T. V., & Saxton, T. K. (2019) · Evolutionary Psychological Science · doi:10.1007/s40806-019-00192-2 · Cited by 89
Robust agents learn causal world models
Richens, J., & Everitt, T. (2024) · arXiv · doi:10.48550/arxiv.2402.10877 · Cited by 5
“…that cannot be generated by interventions include those that change the set of variables V including the decision and utility variables, and introducing selection biases (which are causally represented with the introduction of additional nodes that are conditioned on Bareinboim & Pearl, 2012a).”
Background
Metadata
- Authors
- Bareinboim, E., & Pearl, J.
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2011-08-04
- Volume
- 25
- Issue
- 1
- Pages
- 1754-1755
- ISSN
- 2374-3468
- 2159-5399
- Cited by
- 17
- References
- 3
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- Bareinboim, E., & Pearl, J. (2011). Controlling Selection Bias in Causal Inference. Proceedings of the AAAI Conference on Artificial Intelligence. https://doi.org/10.1609/aaai.v25i1.8056
