Distribution-free predictive inference for individual treatment effects is impossible: any valid set must have infinite expected length under standard assumptions with continuous covariates.
Overlap in observational studies with high-dimensional covariates.Journal of Econometrics, 221(2): 644–654, 2021
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BGM-IV performs nonlinear IV regression by inferring causally structured latent components and replacing the outcome likelihood with an instrument-averaged pseudo-likelihood, showing strongest results in high-dimensional covariate regimes.
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Impossibility of Distribution-Free Predictive Inference for Individual Treatment Effects
Distribution-free predictive inference for individual treatment effects is impossible: any valid set must have infinite expected length under standard assumptions with continuous covariates.
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BGM-IV: an AI-powered Bayesian generative modeling approach for instrumental variable analysis
BGM-IV performs nonlinear IV regression by inferring causally structured latent components and replacing the outcome likelihood with an instrument-averaged pseudo-likelihood, showing strongest results in high-dimensional covariate regimes.