Causal Inference in Water Distribution Networks to Quantify the Effects of Network Damage

Abstract

Water Distribution Networks (WDNs) are engineered systems of interconnected pipes, pumps, and reservoirs that deliver potable water from treatment plants to consumers. These networks are critical to public health but are highly vulnerable to structural damage (e.g., leaks, pipe corrosion), which disrupts water flow and complicates impact prediction. Current methods for assessing damage—such as hydraulic simulations and machine learning—rely on statistical correlations or optimisation, failing to model causal relationships. This limits their ability to predict cascading effects or guide repairs under uncertainty. This study addresses these limitations by applying a causal inference framework for analysing WDNs. The framework leverages graphical causal models to represent the network’s structure and quantifies the impact of damage on water flow predictions. Using Average Treatment Effect (ATE) and Mean Squared Error (MSE) metrics, we analyse how structural damage affects prediction accuracy across different network regions. The framework focuses on three critical areas: source nodes (reservoirs and entry points), mid-network nodes (junction points and main distribution pipes), and consumer nodes (end-user connection points). Experiments on a simulated WDN reveal that damage affecting 40% or more of the network significantly compromises predictive accuracy. Mid-network and consumer nodes prove particularly vulnerable, with damage to these locations causing the greatest disruption to flow predictions. In contrast, source nodes demonstrate greater resilience due to built-in redundancies. Additionally, the study finds that treatment locations closer to outcome variables maintain predictive accuracy longer under damage conditions. By integrating causal inference into WDN analysis, this research provides network operators with a robust methodology for evaluating damage impacts and offers actionable insights for improving network resilience. The findings contribute to both infrastructure management practices and the broader application of causal inference to complex systems analysis.