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PERSPECTIVE article

Front. Immunol.

Sec. Cancer Immunity and Immunotherapy

Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1630781

This article is part of the Research TopicCommunity Series in Novel Reliable Approaches for Prediction and Clinical Decision-making in Cancer: Volume IIView all 10 articles

Correlation Does Not Equal Causation: The Imperative of Causal Inference in Machine Learning Models for Immunotherapy

Provisionally accepted
  • 1the Fourth Hospital of Hebei Medical University, Shijiazhuang 050011, Hebei, China., Shijiazhuang, China
  • 2Department of Pharmacy, Fourth Hospital of Hebei Medical University, Shijiazhuang, China

The final, formatted version of the article will be published soon.

Machine learning (ML) has played a crucial role in advancing precision immunotherapy by integrating multi-omics data to identify biomarkers and predict therapeutic responses. However, a prevalent methodological flaw persists in immunological studies—an overreliance on correlation-based analysis while neglecting causal inference. Traditional ML models struggle to capture the intricate dynamics of immune interactions and often function as "black boxes." A systematic review of 90 studies on immune checkpoint inhibitors revealed that despite employing ML or deep learning techniques, none incorporated causal inference. Similarly, all 36 retrospective studies modeling melanoma exhibited the same limitation. This "knowledge–practice gap" highlights a disconnect: although researchers acknowledge that correlation does not imply causation, causal inference is often omitted in practice. Recent advances in causal ML, like Targeted-BEHRT, CIMLA, and CURE, offer promising solutions. These models can distinguish genuine causal relationships from spurious correlations, integrate multimodal data—including imaging, genomics, and clinical records—and control for unmeasured confounders, thereby enhancing model interpretability and clinical applicability. Nevertheless, practical implementation still faces major challenges, including poor data quality, algorithmic opacity, methodological complexity, and interdisciplinary communication barriers. To bridge these gaps, future efforts must focus on advancing research in causal ML, developing platforms such as the Perturbation Cell Atlas and federated causal learning frameworks, and fostering interdisciplinary training programs. These efforts will be essential to translating causal ML from theoretical innovation to clinical reality in the next 5–10 years—representing not only a methodological upgrade, but also a paradigm shift in immunotherapy research and clinical decision-making.

Keywords: causal inference, machine learning, Immunotherapy, immune checkpointinhibitors, confounding bias, Treatment effect estimation, multimodal data integration, precision medicine

Received: 18 May 2025; Accepted: 01 Sep 2025.

Copyright: © 2025 Wang, Dai, Liang, Hou and Meng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Meng Meng, Department of Pharmacy, Fourth Hospital of Hebei Medical University, Shijiazhuang, China

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