A neural doubly robust proxy causal learning framework using mean embeddings for treatment bridges provides consistent estimators for causal dose-response functions under unobserved confounding for continuous and structured treatments.
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Adaptive specification search in financial machine learning produces statistically significant backtests even when no predictability exists, and a new audit using synthetic null environments plus an absolute magnitude gap can detect and quantify such spurious results.
Proposes adaptive and alternative algorithms to improve the computational efficiency of simulation smoothing for large mixed-frequency VARs in nowcasting applications.
SPICE is a scalable Bayesian MCMC engine for explanatory IRT calibration on sparsely linked persons and items in large assessment banks.
citing papers explorer
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Doubly Robust Proxy Causal Learning with Neural Mean Embeddings
A neural doubly robust proxy causal learning framework using mean embeddings for treatment bridges provides consistent estimators for causal dose-response functions under unobserved confounding for continuous and structured treatments.
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Spurious Predictability in Financial Machine Learning
Adaptive specification search in financial machine learning produces statistically significant backtests even when no predictability exists, and a new audit using synthetic null environments plus an absolute magnitude gap can detect and quantify such spurious results.
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Simulation smoothing for nowcasting with large mixed-frequency VARs
Proposes adaptive and alternative algorithms to improve the computational efficiency of simulation smoothing for large mixed-frequency VARs in nowcasting applications.
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A Scalable Parametric Item Calibration Engine (SPICE) for Explanatory IRT with Sparse Data
SPICE is a scalable Bayesian MCMC engine for explanatory IRT calibration on sparsely linked persons and items in large assessment banks.