Longitudinal Synthetic Data Generation from Causal Structures - Angeruzzi, Alessandro S.; Albertini, Marcelo K. (Anais do Symposium on Knowledge Discovery, Mining and Learning (KDMiLe); 2025: Anais do XIII Symposium on Knowledge Discovery, Mining and LearningDO - 10.5753/kdmile.2025.247519, 2025)
Robust assessment of temporal causal-inference models is hampered by the lack of benchmark datasets whose underlying mechanisms are fully known. We introduce the Causal Synthetic Data Generator (CSDG), an open-source tool that creates longitudinal sequences governed by user-defined structural causal graphs with autoregressive dynamics. By allowing fine-grained control over confounding intensity, treatment policies, intervention timing, and noise, CSDG furnishes a flexible, domain-agnostic test-bed for stress-testing causal-learning algorithms. To demonstrate its utility, we generate synthetic cohorts for a one-step-ahead outcome-forecasting task and compare classical linear regression with encoder-decoder recurrent networks (vanilla RNN, LSTM, and GRU). The results reveal how predictive accuracy degrades as causal complexity increases, underscoring the need for models that explicitly exploit causal structure. Beyond forecasting, CSDG naturally extends to counterfactual data generation and bespoke causal graphs, paving the way for comprehensive, reproducible benchmarks across diverse application contexts.