Sharp bounds on sufficient-cause interactions under the assumption of no redundancy
Abstract Background Sufficient-cause interaction is a type of interaction that has received much attention recently. The sufficient component cause model on which the sufficient-cause interaction is based is however a non-identifiable model. Estimating the interaction parameters from the model is ma...
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Format: | Article |
Language: | English |
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BMC
2017-04-01
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Series: | BMC Medical Research Methodology |
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Online Access: | http://link.springer.com/article/10.1186/s12874-017-0348-y |
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author | Wen-Chung Lee |
author_facet | Wen-Chung Lee |
author_sort | Wen-Chung Lee |
collection | DOAJ |
description | Abstract Background Sufficient-cause interaction is a type of interaction that has received much attention recently. The sufficient component cause model on which the sufficient-cause interaction is based is however a non-identifiable model. Estimating the interaction parameters from the model is mathematically impossible. Methods In this paper, I derive bounding formulae for sufficient-cause interactions under the assumption of no redundancy. Results Two real data sets are used to demonstrate the method (R codes provided). The proposed bounds are sharp and sharper than previous bounds. Conclusions Sufficient-cause interactions can be quantified by setting bounds on them. |
first_indexed | 2024-12-19T18:22:22Z |
format | Article |
id | doaj.art-b0e411a1e381418f8b92002a1c4a83da |
institution | Directory Open Access Journal |
issn | 1471-2288 |
language | English |
last_indexed | 2024-12-19T18:22:22Z |
publishDate | 2017-04-01 |
publisher | BMC |
record_format | Article |
series | BMC Medical Research Methodology |
spelling | doaj.art-b0e411a1e381418f8b92002a1c4a83da2022-12-21T20:10:57ZengBMCBMC Medical Research Methodology1471-22882017-04-011711610.1186/s12874-017-0348-ySharp bounds on sufficient-cause interactions under the assumption of no redundancyWen-Chung Lee0Research Center for Genes, Environment and Human Health and Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan UniversityAbstract Background Sufficient-cause interaction is a type of interaction that has received much attention recently. The sufficient component cause model on which the sufficient-cause interaction is based is however a non-identifiable model. Estimating the interaction parameters from the model is mathematically impossible. Methods In this paper, I derive bounding formulae for sufficient-cause interactions under the assumption of no redundancy. Results Two real data sets are used to demonstrate the method (R codes provided). The proposed bounds are sharp and sharper than previous bounds. Conclusions Sufficient-cause interactions can be quantified by setting bounds on them.http://link.springer.com/article/10.1186/s12874-017-0348-ySufficient component cause modelEpidemiologic methodsCausal inferenceInteractionIdentifiability |
spellingShingle | Wen-Chung Lee Sharp bounds on sufficient-cause interactions under the assumption of no redundancy BMC Medical Research Methodology Sufficient component cause model Epidemiologic methods Causal inference Interaction Identifiability |
title | Sharp bounds on sufficient-cause interactions under the assumption of no redundancy |
title_full | Sharp bounds on sufficient-cause interactions under the assumption of no redundancy |
title_fullStr | Sharp bounds on sufficient-cause interactions under the assumption of no redundancy |
title_full_unstemmed | Sharp bounds on sufficient-cause interactions under the assumption of no redundancy |
title_short | Sharp bounds on sufficient-cause interactions under the assumption of no redundancy |
title_sort | sharp bounds on sufficient cause interactions under the assumption of no redundancy |
topic | Sufficient component cause model Epidemiologic methods Causal inference Interaction Identifiability |
url | http://link.springer.com/article/10.1186/s12874-017-0348-y |
work_keys_str_mv | AT wenchunglee sharpboundsonsufficientcauseinteractionsundertheassumptionofnoredundancy |