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ORIGINAL RESEARCH article

Front. Artif. Intell.

Sec. Medicine and Public Health

Volume 8 - 2025 | doi: 10.3389/frai.2025.1484299

Deep Learning for Causal Inference Using Low Birth Weight in Midwife-Led Continuity Care Intervention in North Shoa Zone, Ethiopia

Provisionally accepted
  • 1Debre Berhan University, Debre Berhan, Ethiopia
  • 2Department of Statistics, Bahir Dar University, Bahir Dar, Amhara Region, Ethiopia
  • 3Department of Data Science, Bahir Dar University, Bahir Dar, Amhara Region, Ethiopia
  • 4Department of Public Health, Debre Berhan University, Debre Berhan, Amhara, Ethiopia
  • 5Department of Midwifery, Debre Berhan University, Debre Berhan, Amhara, Ethiopia

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

Introduction: Low birth weight (LBW), under 2,500g, poses health risks, though not always requiring treatment. Early detection of high-risk pregnancies enables preventive care, improving outcomes for mother and baby. This study aimed to establish cause-and-effect relationships using Causal Deep Learning (CDL) models that reduce bias and estimate heterogeneous treatment effects on LBW in the Midwife-Led Continuity Care (MLCC) intervention. Methods: This study used a quasi-experimental study design (August 2019-September 2020) in North Shoa, Ethiopia, and enrolled 1,166 women divided into two groups: one receiving MLCC and the other receiving other professional groups for comprehensive antenatal/postnatal care. The dataset and code are provided in data availability section. Our model combines counterfactual convolutional neural networks to analyze time-based patterns and Bayesian Ridge regression to reduce bias in propensity scores. We use Counterfactual Regression with Wasserstein Distance (CFR-WASS) and Counterfactual Regression with Maximum Mean Discrepancy (CFR-MMD) to balance patient characteristics and improve counterfactual estimates of treatment effects. This approach strengthens causal insights into how MLCC interventions affect LBW outcomes. Result: The Deep neural networks (DNN) model showed strong predictive accuracy for LBW, with 81.3% training and 81.4% testing performance, an area under the curve (AUC) of 0.88, enabling the reliable early identification of high-risk pregnancies. The study found a strong link between meconium aspiration syndrome (MAS) and LBW (p=0.002), but this does not mean MAS directly causes LBW. MAS likely results from fetal distress or other pregnancy complications that may independently affect LBW. While statistical associations exist, clinical causation remains unproven; therefore, the counterfactual analysis showed MLCC could help reduce LBW risk. CFR-WASS achieved high accuracy (84%) while the precision in heterogeneous treatment effect (PEHE=1.006) and the average treatment effect (ATE=0.24), and CFR-MMD PEHE of 1.02, ATE of 0.45, demonstrating potential for tailored treatment strategies. DNN and multilayer perceptrons uniquely identified key neural weights and biases favoring normal birth weight while suppressing LBW predictions, offering interpretable insights for clinical risk assessment. Conclusion: The CFR-WASS/CFR-MMD model strengthens LBW prediction by identifying crucial factors like MAS and healthcare access, while accurate PEHE and ATE estimates support data-driven prenatal care and targeted interventions for healthier outcomes.

Keywords: Causal Deep Learning, low birth weight, Precision in estimating HeterogeneousTreatment effects, Average treatment effect, midwife-led continuity care

Received: 22 Aug 2024; Accepted: 05 Sep 2025.

Copyright: © 2025 Moges, Tegegne, Mitku, Tesfahun and Hailemeskel. 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: Wudneh Ketema Moges, Debre Berhan University, Debre Berhan, Ethiopia

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