sHAIL-Causal
A specialized variant of the sHAIL framework adapted for causal discovery. It replaces traditional complexity penalties with a joint criterion combining goodness-of-fit saturation and cross-environment invariance, identifying stable sets of causal predictors that remain consistent across different experimental environments.
Timeline (last 90 days)
sHAIL-Causal is a specialized version of the Saturated Hierarchical Atomic Incremental Learning (sHAIL) paradigm for the causal discovery setting
The key contribution of sHAIL-Causal is providing a joint criterion of goodness-of-fit saturation plus cross-environment invariance, replacing the complexity penalty term in SRM
The paper proves that under per-variable Richness conditions, the invariance-gated ladder provably stops at the true causal predictor set
The paper points out that even when the Richness condition holds, naive greedy search fails to recover the true causal set
The failure of greedy search is traced back to the non-monotonicity of the invariance statistic along univariate paths
The proposed remedy combines two elements: bounded exhaustive block seeding and calibrated acceptance thresholds
sHAIL-Causal extends the guarantees to the setting where environments arrive sequentially, providing confidence guarantees that remain valid upon each arrival
A one-shot exhaustive search, in order to maintain equivalent guarantees upon each new environment's arrival, must repeat its full combinatorial search, which is computationally expensive
The guarantees in the sHAIL-Causal paper rely on strong premises, especially the per-variable Richness condition, which may not always be satisfied in real data
sHAIL-Causal is a newly released preprint published on arXiv
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All Facts (11)
The paper concludes formally with the analogy of a wise teacher's intervention, where the teacher can advance a learner to the next level when they stagnate at a plateau
50%UnverifiedThe key contribution of sHAIL-Causal is providing a joint criterion of goodness-of-fit saturation plus cross-environment invariance, replacing the complexity penalty term in SRM
50%UnverifiedThe paper proves that under per-variable Richness conditions, the invariance-gated ladder provably stops at the true causal predictor set
50%UnverifiedThe paper points out that even when the Richness condition holds, naive greedy search fails to recover the true causal set
50%UnverifiedThe failure of greedy search is traced back to the non-monotonicity of the invariance statistic along univariate paths
50%UnverifiedThe proposed remedy combines two elements: bounded exhaustive block seeding and calibrated acceptance thresholds
50%UnverifiedsHAIL-Causal extends the guarantees to the setting where environments arrive sequentially, providing confidence guarantees that remain valid upon each arrival
50%UnverifiedA one-shot exhaustive search, in order to maintain equivalent guarantees upon each new environment's arrival, must repeat its full combinatorial search, which is computationally expensive
50%UnverifiedThe guarantees in the sHAIL-Causal paper rely on strong premises, especially the per-variable Richness condition, which may not always be satisfied in real data
50%UnverifiedsHAIL-Causal is a specialized version of the Saturated Hierarchical Atomic Incremental Learning (sHAIL) paradigm for the causal discovery setting
50%UnverifiedsHAIL-Causal is a newly released preprint published on arXiv
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