Causal Graph Aided Causal Discovery in an Observational Aneurysmal Subarachnoid Hemorrhage Study
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arXiv
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| Natura: | Preprint |
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2024
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| _version_ | 1866916355337355264 |
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| author | Berzuini, Carlo Luciani, Davide Patel, Hiren C. |
| author_facet | Berzuini, Carlo Luciani, Davide Patel, Hiren C. |
| contents | Causal inference methods for observational data are increasingly recognized as a valuable complement to randomized clinical trials (RCTs). They can, under strong assumptions, emulate RCTs or help refine their focus. Our approach to causal inference uses causal directed acyclic graphs (DAGs). We are motivated by a concern that many observational studies in medicine begin without a clear definition of their objectives, without awareness of the scientific potential, and without tools to identify the necessary in itinere adjustments. We present and illustrate methods that provide "midway insights" during study's course, identify meaningful causal questions within the study's reach and point to the necessary data base enhancements for these questions to be meaningfully tackled. The method hinges on concepts of identification and positivity. Concepts are illustrated through an analysis of data generated by patients with aneurysmal Subarachnoid Hemorrhage (aSAH) halfway through a study, focusing in particular on the consequences of external ventricular drain (EVD) in strata of the aSAH population. In addition, we propose a method for multicenter studies, to monitor the impact of changes in practice at an individual center's level, by leveraging principles of instrumental variable (IV) inference. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_06464 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Causal Graph Aided Causal Discovery in an Observational Aneurysmal Subarachnoid Hemorrhage Study Berzuini, Carlo Luciani, Davide Patel, Hiren C. Methodology Machine Learning Causal inference methods for observational data are increasingly recognized as a valuable complement to randomized clinical trials (RCTs). They can, under strong assumptions, emulate RCTs or help refine their focus. Our approach to causal inference uses causal directed acyclic graphs (DAGs). We are motivated by a concern that many observational studies in medicine begin without a clear definition of their objectives, without awareness of the scientific potential, and without tools to identify the necessary in itinere adjustments. We present and illustrate methods that provide "midway insights" during study's course, identify meaningful causal questions within the study's reach and point to the necessary data base enhancements for these questions to be meaningfully tackled. The method hinges on concepts of identification and positivity. Concepts are illustrated through an analysis of data generated by patients with aneurysmal Subarachnoid Hemorrhage (aSAH) halfway through a study, focusing in particular on the consequences of external ventricular drain (EVD) in strata of the aSAH population. In addition, we propose a method for multicenter studies, to monitor the impact of changes in practice at an individual center's level, by leveraging principles of instrumental variable (IV) inference. |
| title | Causal Graph Aided Causal Discovery in an Observational Aneurysmal Subarachnoid Hemorrhage Study |
| topic | Methodology Machine Learning |
| url | https://arxiv.org/abs/2408.06464 |