Correlation vs causation in Alzheimer's disease: an interpretability-driven study

• URL: http://arxiv.org/abs/2506.10179v1
• Date: Wed, 11 Jun 2025 21:10:57 GMT
• Title: Correlation vs causation in Alzheimer's disease: an interpretability-driven study
• Authors: Hamzah Dabool, Raghad Mustafa,
• Abstract summary: This experiment investigates the relationships among clinical, cognitive, genetic, and biomarker features using a combination of correlation analysis, machine learning classification, and model interpretability techniques.<n>We identify key features influencing Alzheimer's disease classification, including cognitive scores and genetic risk factors.<n>Our results highlight that strong correlations do not necessarily imply causation, emphasizing the need for careful interpretation of associative data.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Understanding the distinction between causation and correlation is critical in Alzheimer's disease (AD) research, as it impacts diagnosis, treatment, and the identification of true disease drivers. This experiment investigates the relationships among clinical, cognitive, genetic, and biomarker features using a combination of correlation analysis, machine learning classification, and model interpretability techniques. Employing the XGBoost algorithm, we identified key features influencing AD classification, including cognitive scores and genetic risk factors. Correlation matrices revealed clusters of interrelated variables, while SHAP (SHapley Additive exPlanations) values provided detailed insights into feature contributions across disease stages. Our results highlight that strong correlations do not necessarily imply causation, emphasizing the need for careful interpretation of associative data. By integrating feature importance and interpretability with classical statistical analysis, this work lays groundwork for future causal inference studies aimed at uncovering true pathological mechanisms. Ultimately, distinguishing causal factors from correlated markers can lead to improved early diagnosis and targeted interventions for Alzheimer's disease.

Related papers

• Cross-modal Causal Intervention for Alzheimer's Disease Prediction [12.485088483891843]
  We propose a visual-language causal intervention framework named Alzheimer's Disease Prediction with Cross-modal Causal Intervention.<n>Our framework implicitly eliminates confounders through causal intervention.<n> Experimental results demonstrate the outstanding performance of our method in distinguishing CN/MCI/AD cases.
  arXiv  Detail & Related papers  (2025-07-18T14:21:24Z)
• Causal Explainability of Machine Learning in Heart Failure Prediction from Electronic Health Records [1.1068280788997429]
  The importance of clinical variables in the prognosis of the disease is explained using statistical correlation or machine learning (ML)<n>This paper uses clinical variables from a heart failure (HF) patient cohort to investigate the causal explainability of important variables obtained in statistical and ML contexts.
  arXiv  Detail & Related papers  (2025-06-03T16:46:13Z)
• Quantifying Symptom Causality in Clinical Decision Making: An Exploration Using CausaLM [0.0]
  Current machine learning approaches to medical diagnosis often rely on correlational patterns between symptoms and diseases.<n>In this work, we move beyond correlation to investigate the causal influence of key symptoms-specifically "chest pain" on diagnostic predictions.
  arXiv  Detail & Related papers  (2025-03-25T06:59:21Z)
• Challenges in Variable Importance Ranking Under Correlation [6.718144470265263]
  We present a comprehensive simulation study investigating the impact of feature correlation on the assessment of variable importance. While there is always no correlation between knockoff variables and its corresponding predictor variables, we prove that the correlation increases linearly beyond a certain correlation threshold between the predictor variables.
  arXiv  Detail & Related papers  (2024-02-05T19:02:13Z)
• Identifiable Latent Polynomial Causal Models Through the Lens of Change [82.14087963690561]
  Causal representation learning aims to unveil latent high-level causal representations from observed low-level data.<n>One of its primary tasks is to provide reliable assurance of identifying these latent causal models, known as identifiability.
  arXiv  Detail & Related papers  (2023-10-24T07:46:10Z)
• A Causal Framework for Decomposing Spurious Variations [68.12191782657437]
  We develop tools for decomposing spurious variations in Markovian and Semi-Markovian models. We prove the first results that allow a non-parametric decomposition of spurious effects. The described approach has several applications, ranging from explainable and fair AI to questions in epidemiology and medicine.
  arXiv  Detail & Related papers  (2023-06-08T09:40:28Z)
• On Learning Necessary and Sufficient Causal Graphs [29.339455706346193]
  In practice, only a small subset of variables in the graph are relevant to the outcomes of interest. We propose learning a class of necessary and sufficient causal graphs (NSCG) that exclusively comprises causally relevant variables for an outcome of interest. We develop a necessary and sufficient causal structural learning (NSCSL) algorithm, by establishing theoretical properties and relationships between probabilities of causation and natural causal effects of features.
  arXiv  Detail & Related papers  (2023-01-29T08:19:15Z)
• Identifying Weight-Variant Latent Causal Models [82.14087963690561]
  We find that transitivity acts as a key role in impeding the identifiability of latent causal representations. Under some mild assumptions, we can show that the latent causal representations can be identified up to trivial permutation and scaling. We propose a novel method, termed Structural caUsAl Variational autoEncoder, which directly learns latent causal representations and causal relationships among them.
  arXiv  Detail & Related papers  (2022-08-30T11:12:59Z)
• Typing assumptions improve identification in causal discovery [123.06886784834471]
  Causal discovery from observational data is a challenging task to which an exact solution cannot always be identified. We propose a new set of assumptions that constrain possible causal relationships based on the nature of the variables.
  arXiv  Detail & Related papers  (2021-07-22T14:23:08Z)
• Towards Causal Representation Learning [96.110881654479]
  The two fields of machine learning and graphical causality arose and developed separately. There is now cross-pollination and increasing interest in both fields to benefit from the advances of the other.
  arXiv  Detail & Related papers  (2021-02-22T15:26:57Z)
• Estimation of Causal Effects in the Presence of Unobserved Confounding in the Alzheimer's Continuum [3.2489082010225494]
  We derive a causal graph from the current clinical knowledge on cause and effect in the Alzheimer's disease continuum. We show that identifiability of the causal effect requires all confounders to be known and measured. In our theoretical analysis, we prove that using the substitute confounder enables identifiability of the causal effect of neuroanatomy on cognition.
  arXiv  Detail & Related papers  (2020-06-23T16:29:54Z)
• Dynamic Graph Correlation Learning for Disease Diagnosis with Incomplete Labels [66.57101219176275]
  Disease diagnosis on chest X-ray images is a challenging multi-label classification task. We propose a Disease Diagnosis Graph Convolutional Network (DD-GCN) that presents a novel view of investigating the inter-dependency among different diseases. Our method is the first to build a graph over the feature maps with a dynamic adjacency matrix for correlation learning.
  arXiv  Detail & Related papers  (2020-02-26T17:10:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.