<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Linear regression with temporally delayed covariates (distributed-lag linear regression) is a standard approach to lag exposure assessment, but it is limited to a single biomarker of interest and cannot provide insights on the relationships holding among the pathogen exposures, thus precluding the assessment of causal effects in a general context. In this paper, to overcome these limitations, distributed-lag linear regression is applied to Markovian structural causal models. Dynamic causal effects are defined as a function of regression coefficients at different time lags. The proposed methodology is illustrated using a simple lag exposure assessment problem.</p></abstract>

Linear regression with temporally delayed covariates (distributed-lag linear regression) is a standard approach to lag exposure assessment, but it is limited to a single biomarker of interest and cannot provide insights on the relationships holding among the pathogen exposures, thus precluding the assessment of causal effects in a general context. In this paper, to overcome these limitations, distributed-lag linear regression is applied to Markovian structural causal models. Dynamic causal effects are defined as a function of regression coefficients at different time lags. The proposed methodology is illustrated using a simple lag exposure assessment problem.

Linear Markovian models for lag exposure assessment

Department of Statistics, Computer Science, Applications

Biometrical Letters

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Linear Markovian models for lag exposure assessment"}

Linear regression with temporally delayed covariates (distributed-lag linear regression) is a standard approach to lag exposure assessment, but it is limited...

Linear Markovian models for lag exposure assessment

Data publikacji: 14 gru 2018

Zakres stron: 179 - 195

DOI: https://doi.org/10.2478/bile-2018-0012

Słowa kluczowe
directed acyclic graph, distributed-lag linear regression, dynamic causal inference, structural causal models, polynomial lag shape

© 2018 Alessandro Magrini, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Linear Markovian models for lag exposure assessment

Data publikacji: 14 gru 2018

Zakres stron: 179 - 195

DOI: https://doi.org/10.2478/bile-2018-0012

Słowa kluczowedirected acyclic graph, distributed-lag linear regression, dynamic causal inference, structural causal models, polynomial lag shape

© 2018 Alessandro Magrini, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Słowa kluczowe
directed acyclic graph, distributed-lag linear regression, dynamic causal inference, structural causal models, polynomial lag shape