Causal Representation Learning for Cyber-Physical Systems Engineering Systems in Kenya: Out-of-Distribution Benchmarking and Error Analysis

Cyber-physical systems deployed in Kenyan critical infrastructure operate under distribution shifts that are poorly represented in conventional machine learning benchmarks. We define a causal representation learning problem for dynamical systems, establish identifiability conditions under weak intervention assumptions, and propose a benchmarking methodology that separates covariate shift, mechanism shift, and feedback-induced shift. The central contribution is an error taxonomy that distinguishes representation error, causal mechanism error, and system-level generalisation error, each with distinct diagnostic signatures and mitigation strategies. We analyse how these error classes manifest in Kenyan contexts such as grid-edge solar inverters, water distribution telemetry, and agricultural cold-chain monitoring, where data scarcity and environmental variability amplify the consequences of misspecified representations. The framework yields testable propositions for system design and evaluation, and clarifies why generic domain adaptation methods underperform when the underlying causal graph is unknown. We conclude that out-of-distribution benchmarking for cyber-physical systems must be organised around causal structure rather than marginal distribution statistics.