EcoTable is the first NL-based data integration framework that builds a join-likelihood graph, uses two-stage schema linking and Steiner tree search to find paths, then generates transformations with LLMs, reporting >30% accuracy gain and 5x lower cost on four real-world datasets.
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Develops TWSF estimator for causal forecasting in panel data by combining synthetic controls with time-series models under low-rank latent factor assumptions, providing finite-sample bounds and asymptotic normality.
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EcoTable: Cost-effective Table Integration in Data Lakes for Natural Language Queries
EcoTable is the first NL-based data integration framework that builds a join-likelihood graph, uses two-stage schema linking and Steiner tree search to find paths, then generates transformations with LLMs, reporting >30% accuracy gain and 5x lower cost on four real-world datasets.
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Causal Forecasting in Panel Data: A Two-Way Synthetic Forecasting Approach
Develops TWSF estimator for causal forecasting in panel data by combining synthetic controls with time-series models under low-rank latent factor assumptions, providing finite-sample bounds and asymptotic normality.