Distribution-free predictive inference for individual treatment effects is impossible: any valid set must have infinite expected length under standard assumptions with continuous covariates.
Statistics and causal inference.Journal of the American Statistical Association, 81(396):945–960, 1986
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TabPFN-3 delivers state-of-the-art tabular prediction performance on benchmarks up to 1M rows, is up to 20x faster than prior versions, and introduces test-time scaling that beats non-TabPFN models by hundreds of Elo points.
Action-outcome probabilities for rational choice can be grounded in causal models both when the causal structure is known and when it is unknown, with an extension to causal Nash Equilibrium.
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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TabPFN-3: Technical Report
TabPFN-3 delivers state-of-the-art tabular prediction performance on benchmarks up to 1M rows, is up to 20x faster than prior versions, and introduces test-time scaling that beats non-TabPFN models by hundreds of Elo points.
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Choosing with unknown causal information: Action-outcome probabilities for decision making can be grounded in causal models
Action-outcome probabilities for rational choice can be grounded in causal models both when the causal structure is known and when it is unknown, with an extension to causal Nash Equilibrium.