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A avaliação robusta de modelos de inferência causal temporal é dificultada pela ausência de conjuntos de dados de benchmark cujos mecanismos subjacentes sejam totalmente conhecidos. Apresentamos o Causal Synthetic Data Generator (CSDG), uma ferramenta de código aberto que gera sequências longitudinais governadas por grafos causais estruturais definidos pelo usuário, com dinâmica autorregressiva. Ao permitir um controle refinado sobre a intensidade de confundimento, políticas de tratamento, momento de intervenção e ruído, o CSDG fornece um ambiente de testes flexível e independente de domínio para avaliar algoritmos de aprendizado causal sob condições de estresse. Para demonstrar sua utilidade, geramos coortes sintéticos para uma tarefa de previsão de desfechos de um passo à frente e comparamos a regressão linear clássica com redes recorrentes do tipo encoder-decoder (RNN, LSTM e GRU). Os resultados revelam como a acurácia preditiva degrada à medida que a complexidade causal aumenta, destacando a necessidade de modelos que explorem explicitamente a estrutura causal. Além da previsão, o CSDG se estende naturalmente à geração de dados contrafactuais e à construção de grafos causais personalizados, abrindo caminho para benchmarks abrangentes e reprodutíveis em diversos contextos de aplicação.

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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.

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1	Alessandro Angeruzzi(alessandro.angeruzzi@ufu.br)
2	Marcelo Albertini(albertini@ufu.br)

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