A neural network simulation–regression method was developed to assess the impact of sanctions on the level of administrative offences under fragmented, noisy, and short administrative time series. The study addresses the problem of quantifying and predicting changes at the offence level as a sanction size function, using detection probability, prior violation level, compliance costs, and auxiliary contextual factors. The proposed framework combines a hybrid MLP–LSTM neural network, double machine learning-based orthogonal causal estimation, the simulation-based generation of counterfactual scenarios through domain randomization, multiple imputation for missing data, debiasing procedures, and ensemble uncertainty estimation. The contribution to administrative law consists of a quantitative tool creation for substantiating and optimising sanction policy, assessing heterogeneous effects, and supporting evidence-based rulemaking and law enforcement decisions. In comparative experiments, the developed method achieved an RMSE of 8…12%, a prediction accuracy of 93…96%, an overall accuracy of 95%, a precision of 94%, a recall of 93%, and an F1-score of 93.5%, thereby outperforming contemporary econometric, simulation, causal machine learning, and predictive machine learning approaches used for sanction effect modelling. Additional verification through nonparametric statistical testing cponfirmed that the proposed model’s superiority over the compared algorithms is statistically significant across the main quality metrics, which strengthens the evidence for its robustness and practical value in sanction policy analysis under fragmented administrative data conditions.
Vladov et al. (Mon,) studied this question.