Causal AI with Real World Data: Do Statins Protect from Alzheimer’s Disease Onset?

Details

• Alternative Title:
  ICMHI 2021 (2021)
• Personal Author:
  Prosperi, Mattia ; Salemi, Marco ; Ghosh, Shantanu ; Lyu, Tianchen ; Bian, Jiang ; Chen, Zhaoyi ; Zhao, Jinying
• Description:
  Causal artificial intelligence aims at developing bias-robust models that can be used to intervene on, rather than just be predictive, of risks or outcomes. However, learning interventional models from observational data, including electronic health records (EHR), is challenging due to inherent bias, e.g., protopathic, confounding, collider. When estimating the effects of treatment interventions, classical approaches like propensity score matching are often used, but they pose limitations with large feature sets, nonlinear/nonparallel treatment group assignments, and collider bias. In this work, we used data from a large EHR consortium -OneFlorida- and evaluated causal statistical/machine learning methods for determining the effect of statin treatment on the risk of Alzheimer's disease, a debated clinical research question. We introduced a combination of directed acyclic graph (DAG) learning and comparison with expert's design, with calculation of the generalized adjustment criterion (GAC), to find an optimal set of covariates for estimation of treatment effects -ameliorating collider bias. The DAG/CAC approach was assessed together with traditional propensity score matching, inverse probability weighting, virtual-twin/counterfactual random forests, and deep counterfactual networks. We showed large heterogeneity in effect estimates upon different model configurations. Our results did not exclude a protective effect of statins, where the DAG/GAC point estimate aligned with the maximum credibility estimate, although the 95% credibility interval included a null effect, warranting further studies and replication.
• Keywords:
  Bayesian Network Biomedical Informatics Causal Artificial Intelligence Directed Acyclic Graph Electronic Medical Records Generalized Adjustment Criterion Machine Learning Treatment Effect
• Source:
  ICMHI 2021 (2021). 2021:296-303
• Pubmed ID:
  37954527
• Pubmed Central ID:
  PMC10636706
• Document Type:
  Journal Article
• Funding:
  U18 DP006512/DP/NCCDPHP CDC HHSUnited States/ ; R21 CA245858/CA/NCI NIH HHSUnited States/ ; UL1 TR001427/TR/NCATS NIH HHSUnited States/ ; R01 CA246418/CA/NCI NIH HHSUnited States/ ; U18DP006512/ACL/ACL HHSUnited States/ ; R21 AG068717/AG/NIA NIH HHSUnited States/
• Volume:
  2021
• Collection(s):
  CDC Public Access
• Main Document Checksum:
  urn:sha256:7f1f165d14eed711cb7f0e56c5ab23558f1a2b274a4f335420511e7b9d19afe6
• Download URL:
  https://stacks.cdc.gov/view/cdc/135490/cdc_135490_DS1.pdf
• File Type:
  [PDF - 967.63 KB ]