Distribution-free predictive inference for individual treatment effects is impossible: any valid set must have infinite expected length under standard assumptions with continuous covariates.
Towards optimal doubly robust estimation of heterogeneous causal effects
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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The authors develop a two-stage orthogonal learning framework using graph neural networks to estimate heterogeneous direct and spillover causal effects on networks, along with bootstrap-based uncertainty quantification.
Physical activity's protective association with lower mental distress strengthens monotonically with age and has eroded to null for young adults over the past decade.
citing papers explorer
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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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Estimating Heterogeneous Causal Effect on Networks via Orthogonal Learning
The authors develop a two-stage orthogonal learning framework using graph neural networks to estimate heterogeneous direct and spillover causal effects on networks, along with bootstrap-based uncertainty quantification.
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Age-Dependent Heterogeneity in the Association Between Physical Activity and Mental Distress: A Causal Machine Learning Analysis of 3.2 Million U.S. Adults
Physical activity's protective association with lower mental distress strengthens monotonically with age and has eroded to null for young adults over the past decade.