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Australian Institute for Machine Learning, Adelaide University
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TimeLAVA reveals that a learning-agnostic approach can significantly enhance data valuation in time series, outperforming traditional methods by effectively capturing temporal dynamics.
Why start from scratch when observational data can give you a head start? This paper shows how to design better causal experiments by actively learning *residual* biases, not the whole causal model.
Gaussian Processes can now provide reliable uncertainty estimates for causal inference with instrumental variables, addressing a critical gap in existing methods.
