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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PerturbedVAE disentangles perturbation-specific signals from invariant gene expression structure to recover causal representations and improve out-of-distribution prediction in single-cell perturbation modeling.
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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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What Makes a Representation Good for Single-Cell Perturbation Prediction?
PerturbedVAE disentangles perturbation-specific signals from invariant gene expression structure to recover causal representations and improve out-of-distribution prediction in single-cell perturbation modeling.