Unverified50% confidenceFactExact time
The core conclusion of this study is: the covariate distribution alone being insufficiently smooth is enough to slow down the convergence rate of pointwise CATE estimation, without the treatment effect function or the nuisance function needing to become rougher.
1
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50%
Confidence
Long-term
Relevance
10/14/2026
First Seen
Sources
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Related Claims
UnverifiedEven under exactly the same smoothness assumptions on the treatment effect function and the nuisance function, a rough covariate design leads to slower pointwise convergence rates for CATE estimation than a uniform design.79% similarUnverifiedResearch reveals that LLMs do not reliably weight and update based on evidence reliability like an ideal Bayesian agent, but instead exhibit behavioral patterns close to distributional skew, with their influence being less correlated with the true reliability of evidence than the Bayesian framework would predict.79% similarUnverifiedThe slow decline in divergence is traced to the smoothing of rare contexts, as increasing the amount of data cannot effectively reduce sampling variance in sparse regions.76% similarUnverifiedNumerical experiments show that when handling non-smooth and heavy-tailed target distributions, the non-reversible versions exhibit faster sampling efficiency compared to their reversible counterparts.75% similarUnverifiedA larger smoothness parameter β indicates a smoother density function, theoretically enabling better estimation accuracy with fewer samples.75% similar
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