Causes of Outcome Learning: a causal inference-inspired machine learning approach to disentangling common combinations of potential causes of a health outcome

Research output: Contribution to journalJournal articlepeer-review

Standard

Causes of Outcome Learning : a causal inference-inspired machine learning approach to disentangling common combinations of potential causes of a health outcome. / Rieckmann, Andreas; Dworzynski, Piotr; Arras, Leila; Lapuschkin, Sebastian; Samek, Wojciech; Arah, Onyebuchi Aniweta; Rod, Naja Hulvej; Ekstrøm, Claus Thorn.

In: International Journal of Epidemiology, Vol. 51, No. 5, 2022, p. 1622–1636.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Rieckmann, A, Dworzynski, P, Arras, L, Lapuschkin, S, Samek, W, Arah, OA, Rod, NH & Ekstrøm, CT 2022, 'Causes of Outcome Learning: a causal inference-inspired machine learning approach to disentangling common combinations of potential causes of a health outcome', International Journal of Epidemiology, vol. 51, no. 5, pp. 1622–1636. https://doi.org/10.1093/ije/dyac078

APA

Rieckmann, A., Dworzynski, P., Arras, L., Lapuschkin, S., Samek, W., Arah, O. A., Rod, N. H., & Ekstrøm, C. T. (2022). Causes of Outcome Learning: a causal inference-inspired machine learning approach to disentangling common combinations of potential causes of a health outcome. International Journal of Epidemiology, 51(5), 1622–1636. https://doi.org/10.1093/ije/dyac078

Vancouver

Rieckmann A, Dworzynski P, Arras L, Lapuschkin S, Samek W, Arah OA et al. Causes of Outcome Learning: a causal inference-inspired machine learning approach to disentangling common combinations of potential causes of a health outcome. International Journal of Epidemiology. 2022;51(5):1622–1636. https://doi.org/10.1093/ije/dyac078

Author

Rieckmann, Andreas ; Dworzynski, Piotr ; Arras, Leila ; Lapuschkin, Sebastian ; Samek, Wojciech ; Arah, Onyebuchi Aniweta ; Rod, Naja Hulvej ; Ekstrøm, Claus Thorn. / Causes of Outcome Learning : a causal inference-inspired machine learning approach to disentangling common combinations of potential causes of a health outcome. In: International Journal of Epidemiology. 2022 ; Vol. 51, No. 5. pp. 1622–1636.

Bibtex

@article{43c6c3a210a74237b053fd106f686c40,
title = "Causes of Outcome Learning: a causal inference-inspired machine learning approach to disentangling common combinations of potential causes of a health outcome",
abstract = "Nearly all diseases are caused by different combinations of exposures. Yet, most epidemiological studies focus on estimating the effect of a single exposure on a health outcome. We present the Causes of Outcome Learning approach (CoOL), which seeks to discover combinations of exposures that lead to an increased risk of a specific outcome in parts of the population. The approach allows for exposures acting alone and in synergy with others. The road map of CoOL involves (i) a pre-computational phase used to define a causal model; (ii) a computational phase with three steps, namely (a) fitting a non-negative model on an additive scale, (b) decomposing risk contributions and (c) clustering individuals based on the risk contributions into subgroups; and (iii) a post-computational phase on hypothesis development, validation and triangulation using new data before eventually updating the causal model. The computational phase uses a tailored neural network for the non-negative model on an additive scale and layer-wise relevance propagation for the risk decomposition through this model. We demonstrate the approach on simulated and real-life data using the R package 'CoOL'. The presentation focuses on binary exposures and outcomes but can also be extended to other measurement types. This approach encourages and enables researchers to identify combinations of exposures as potential causes of the health outcome of interest. Expanding our ability to discover complex causes could eventually result in more effective, targeted and informed interventions prioritized for their public health impact.",
author = "Andreas Rieckmann and Piotr Dworzynski and Leila Arras and Sebastian Lapuschkin and Wojciech Samek and Arah, {Onyebuchi Aniweta} and Rod, {Naja Hulvej} and Ekstr{\o}m, {Claus Thorn}",
note = "{\textcopyright} The Author(s) 2022; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.",
year = "2022",
doi = "10.1093/ije/dyac078",
language = "English",
volume = "51",
pages = "1622–1636",
journal = "International Journal of Epidemiology",
issn = "0300-5771",
publisher = "Oxford University Press",
number = "5",

}

RIS

TY - JOUR

T1 - Causes of Outcome Learning

T2 - a causal inference-inspired machine learning approach to disentangling common combinations of potential causes of a health outcome

AU - Rieckmann, Andreas

AU - Dworzynski, Piotr

AU - Arras, Leila

AU - Lapuschkin, Sebastian

AU - Samek, Wojciech

AU - Arah, Onyebuchi Aniweta

AU - Rod, Naja Hulvej

AU - Ekstrøm, Claus Thorn

N1 - © The Author(s) 2022; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.

PY - 2022

Y1 - 2022

N2 - Nearly all diseases are caused by different combinations of exposures. Yet, most epidemiological studies focus on estimating the effect of a single exposure on a health outcome. We present the Causes of Outcome Learning approach (CoOL), which seeks to discover combinations of exposures that lead to an increased risk of a specific outcome in parts of the population. The approach allows for exposures acting alone and in synergy with others. The road map of CoOL involves (i) a pre-computational phase used to define a causal model; (ii) a computational phase with three steps, namely (a) fitting a non-negative model on an additive scale, (b) decomposing risk contributions and (c) clustering individuals based on the risk contributions into subgroups; and (iii) a post-computational phase on hypothesis development, validation and triangulation using new data before eventually updating the causal model. The computational phase uses a tailored neural network for the non-negative model on an additive scale and layer-wise relevance propagation for the risk decomposition through this model. We demonstrate the approach on simulated and real-life data using the R package 'CoOL'. The presentation focuses on binary exposures and outcomes but can also be extended to other measurement types. This approach encourages and enables researchers to identify combinations of exposures as potential causes of the health outcome of interest. Expanding our ability to discover complex causes could eventually result in more effective, targeted and informed interventions prioritized for their public health impact.

AB - Nearly all diseases are caused by different combinations of exposures. Yet, most epidemiological studies focus on estimating the effect of a single exposure on a health outcome. We present the Causes of Outcome Learning approach (CoOL), which seeks to discover combinations of exposures that lead to an increased risk of a specific outcome in parts of the population. The approach allows for exposures acting alone and in synergy with others. The road map of CoOL involves (i) a pre-computational phase used to define a causal model; (ii) a computational phase with three steps, namely (a) fitting a non-negative model on an additive scale, (b) decomposing risk contributions and (c) clustering individuals based on the risk contributions into subgroups; and (iii) a post-computational phase on hypothesis development, validation and triangulation using new data before eventually updating the causal model. The computational phase uses a tailored neural network for the non-negative model on an additive scale and layer-wise relevance propagation for the risk decomposition through this model. We demonstrate the approach on simulated and real-life data using the R package 'CoOL'. The presentation focuses on binary exposures and outcomes but can also be extended to other measurement types. This approach encourages and enables researchers to identify combinations of exposures as potential causes of the health outcome of interest. Expanding our ability to discover complex causes could eventually result in more effective, targeted and informed interventions prioritized for their public health impact.

U2 - 10.1093/ije/dyac078

DO - 10.1093/ije/dyac078

M3 - Journal article

C2 - 35526156

VL - 51

SP - 1622

EP - 1636

JO - International Journal of Epidemiology

JF - International Journal of Epidemiology

SN - 0300-5771

IS - 5

ER -

ID: 307100843