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Bayesian identification of structural coefficients in causal models and the causal false-positive risk of confounders and colliders in linear Markovian models
BACKGROUND: Causal inference has seen an increasing popularity in medical research. Estimation of causal effects from observational data allows to draw conclusions from data when randomized controlled trials cannot be conducted. Although the identification of structural causal models (SCM) and the c...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8883695/ https://www.ncbi.nlm.nih.gov/pubmed/35220960 http://dx.doi.org/10.1186/s12874-021-01473-w |
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author | Kelter, Riko |
author_facet | Kelter, Riko |
author_sort | Kelter, Riko |
collection | PubMed |
description | BACKGROUND: Causal inference has seen an increasing popularity in medical research. Estimation of causal effects from observational data allows to draw conclusions from data when randomized controlled trials cannot be conducted. Although the identification of structural causal models (SCM) and the calculation of structural coefficients has received much attention, a key requirement for valid causal inference is that conclusions are drawn based on the true data-generating model. METHODS: It remains widely unknown how large the probability is to reject the true structural causal model when observational data from it is sampled. The latter probability – the causal false-positive risk – is crucial, as rejection of the true causal model can induce bias in the estimation of causal effects. In this paper, the widely used causal models of confounders and colliders are studied regarding their causal false-positive risk in linear Markovian models. A simulation study is carried out which investigates the causal false-positive risk in Gaussian linear Markovian models. Therefore, the testable implications of the DAG corresponding to confounders and colliders are analyzed from a Bayesian perspective. Furthermore, the induced bias in estimating the structural coefficients and causal effects is studied. RESULTS: Results show that the false-positive risk of rejecting a true SCM of even simple building blocks like confounders and colliders is substantial. Importantly, estimation of average, direct and indirect causal effects can become strongly biased if a true model is rejected. The causal false-positive risk may thus serve as an indicator or proxy for the induced bias. CONCLUSION: While the identification of structural coefficients and testable implications of causal models have been studied rigorously in the literature, this paper shows that causal inference also must develop new concepts for controlling the causal false-positive risk. Although a high risk cannot be equated with a substantial bias, it is indicative of the induced bias. The latter fact calls for the development of more advanced risk measures for committing a causal type I error in causal inference. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (10.1186/s12874-021-01473-w). |
format | Online Article Text |
id | pubmed-8883695 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-88836952022-03-07 Bayesian identification of structural coefficients in causal models and the causal false-positive risk of confounders and colliders in linear Markovian models Kelter, Riko BMC Med Res Methodol Research BACKGROUND: Causal inference has seen an increasing popularity in medical research. Estimation of causal effects from observational data allows to draw conclusions from data when randomized controlled trials cannot be conducted. Although the identification of structural causal models (SCM) and the calculation of structural coefficients has received much attention, a key requirement for valid causal inference is that conclusions are drawn based on the true data-generating model. METHODS: It remains widely unknown how large the probability is to reject the true structural causal model when observational data from it is sampled. The latter probability – the causal false-positive risk – is crucial, as rejection of the true causal model can induce bias in the estimation of causal effects. In this paper, the widely used causal models of confounders and colliders are studied regarding their causal false-positive risk in linear Markovian models. A simulation study is carried out which investigates the causal false-positive risk in Gaussian linear Markovian models. Therefore, the testable implications of the DAG corresponding to confounders and colliders are analyzed from a Bayesian perspective. Furthermore, the induced bias in estimating the structural coefficients and causal effects is studied. RESULTS: Results show that the false-positive risk of rejecting a true SCM of even simple building blocks like confounders and colliders is substantial. Importantly, estimation of average, direct and indirect causal effects can become strongly biased if a true model is rejected. The causal false-positive risk may thus serve as an indicator or proxy for the induced bias. CONCLUSION: While the identification of structural coefficients and testable implications of causal models have been studied rigorously in the literature, this paper shows that causal inference also must develop new concepts for controlling the causal false-positive risk. Although a high risk cannot be equated with a substantial bias, it is indicative of the induced bias. The latter fact calls for the development of more advanced risk measures for committing a causal type I error in causal inference. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (10.1186/s12874-021-01473-w). BioMed Central 2022-02-27 /pmc/articles/PMC8883695/ /pubmed/35220960 http://dx.doi.org/10.1186/s12874-021-01473-w Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Kelter, Riko Bayesian identification of structural coefficients in causal models and the causal false-positive risk of confounders and colliders in linear Markovian models |
title | Bayesian identification of structural coefficients in causal models and the causal false-positive risk of confounders and colliders in linear Markovian models |
title_full | Bayesian identification of structural coefficients in causal models and the causal false-positive risk of confounders and colliders in linear Markovian models |
title_fullStr | Bayesian identification of structural coefficients in causal models and the causal false-positive risk of confounders and colliders in linear Markovian models |
title_full_unstemmed | Bayesian identification of structural coefficients in causal models and the causal false-positive risk of confounders and colliders in linear Markovian models |
title_short | Bayesian identification of structural coefficients in causal models and the causal false-positive risk of confounders and colliders in linear Markovian models |
title_sort | bayesian identification of structural coefficients in causal models and the causal false-positive risk of confounders and colliders in linear markovian models |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8883695/ https://www.ncbi.nlm.nih.gov/pubmed/35220960 http://dx.doi.org/10.1186/s12874-021-01473-w |
work_keys_str_mv | AT kelterriko bayesianidentificationofstructuralcoefficientsincausalmodelsandthecausalfalsepositiveriskofconfoundersandcollidersinlinearmarkovianmodels |