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AutoCD\u2019s goal is to deliver all causal information that an expert human analyst would provide and answer user\u2019s causal queries. To this goal, we introduce ETIA, a system that performs dimensionality reduction, causal structure learning, and causal reasoning. We present the architecture of ETIA, benchmark its performance on synthetic data sets, and present a use case example. 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