Conformal prediction: A unified review of theory and new challenges Page 1 Bernoulli 29(1),
2023, 1–23 https://doi.org/10.3150/21-BEJ1447 Conformal prediction: A unified review of …

Conformal prediction is a technique for constructing prediction intervals that attain valid
coverage in finite samples, without making distributional assumptions. Despite this appeal …

We extend conformal prediction methodology beyond the case of exchangeable data. In
particular, we show that a weighted version of conformal prediction can be used to compute …

Abstract Conformal inference, cross-validation+, and the jackknife+ are hold-out methods
that can be combined with virtually any machine learning algorithm to construct prediction …

We develop a general framework for distribution-free predictive inference in regression,
using conformal inference. The proposed methodology allows for the construction of a …

Evaluating treatment effect heterogeneity widely informs treatment decision making. At the
moment, much emphasis is placed on the estimation of the conditional average treatment …

We propose a model-free framework for sensitivity analysis of individual treatment effects
(ITEs), building upon ideas from conformal inference. For any unit, our procedure reports the …

We introduce new inference procedures for counterfactual and synthetic control methods for
policy evaluation. We recast the causal inference problem as a counterfactual prediction and …

Vladimir Vovk Alexander Gammerman Glenn Shafer Second Edition Page 1 Vladimir Vovk
Alexander Gammerman Glenn Shafer Algorithmic Learning in a Random World Second …

We study distribution-free, non-parametric prediction bands with a focus on their finite
sample behaviour. First we investigate and develop different notions of finite sample …