Two auxiliary environments suffice to identify causal graphs and functional mechanisms in structural causal models under acyclicity and invariance assumptions, enabling correct counterfactual inference.
Mooij and Dominik Janzing and Bernhard Sch
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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DAGgr aggregates weighted candidate DAGs using out-of-sample predictive likelihood and an acyclicity-preserving threshold, with claimed finite-sample bounds and consistency, outperforming baselines in simulations and protein network data.
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Causal Learning with the Invariance Principle
Two auxiliary environments suffice to identify causal graphs and functional mechanisms in structural causal models under acyclicity and invariance assumptions, enabling correct counterfactual inference.
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Stable Causal Discovery via Directed Acyclic Graph Aggregation
DAGgr aggregates weighted candidate DAGs using out-of-sample predictive likelihood and an acyclicity-preserving threshold, with claimed finite-sample bounds and consistency, outperforming baselines in simulations and protein network data.