TabOrder learns unsupervised causal variable orderings and enforces them with order-constrained attention for tabular prediction and imputation under distribution shifts.
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Identifiability is proven for recurrent nonlinear switching dynamical systems under flexible assumptions, and ΩSDS is introduced as a flow-based estimator that improves disentanglement and forecasting over VAE-based methods.
LiMIAM and DirectLiMIAM enable causal discovery from observational data under mean-independent but dependent disturbances, outperforming LiNGAM in simulations and recovering plausible orderings in oil market data.
A new directed tree structure learning framework for zero-inflated compositional nodes uses KL divergence scoring and column-stochastic transition matrices for conditional expectations, with proven consistency and finite-sample guarantees.
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.
DAG-DC-ADMM jointly clusters subjects and learns their cluster-specific causal DAGs via structural equation modeling, groupwise truncated Lasso fusion penalties, and an ADMM solver for the resulting nonconvex problem.
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.
TriOpt recovers topological order via Sherman-Morrison downdates on linear kernels then solves a convex program for the DAG edges, claiming exact recovery under the true order and large speedups on high-dimensional data.
CausalMix fits a causal model on 512 runs of a 0.5B model to estimate CATE, then extrapolates optimal mixtures for an 800K data pool applied to 7B and 4B models, outperforming RegMix.
The paper defines possibility space, timing computation, and causal factum to make timing a computable variable, and illustrates the framework with automatic trajectory discovery and counterfactual timing on 3,276 breast-cancer patients.
citing papers explorer
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Learning Causal Orderings for In-Context Tabular Prediction
TabOrder learns unsupervised causal variable orderings and enforces them with order-constrained attention for tabular prediction and imputation under distribution shifts.
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End-to-End Identifiable and Consistent Recurrent Switching Dynamical Systems
Identifiability is proven for recurrent nonlinear switching dynamical systems under flexible assumptions, and ΩSDS is introduced as a flow-based estimator that improves disentanglement and forecasting over VAE-based methods.
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Causal discovery under mean independence and linearity
LiMIAM and DirectLiMIAM enable causal discovery from observational data under mean-independent but dependent disturbances, outperforming LiNGAM in simulations and recovering plausible orderings in oil market data.
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Structure Learning for Directed Trees with Zero-Inflated Compositional Nodes
A new directed tree structure learning framework for zero-inflated compositional nodes uses KL divergence scoring and column-stochastic transition matrices for conditional expectations, with proven consistency and finite-sample guarantees.
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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.
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A Unified Framework for Structure-Aware Clustering and Heterogeneous Causal Graph Learning
DAG-DC-ADMM jointly clusters subjects and learns their cluster-specific causal DAGs via structural equation modeling, groupwise truncated Lasso fusion penalties, and an ADMM solver for the resulting nonconvex problem.
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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.
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TriOpt: A Scalable Algorithm for Linear Causal Discovery
TriOpt recovers topological order via Sherman-Morrison downdates on linear kernels then solves a convex program for the DAG edges, claiming exact recovery under the true order and large speedups on high-dimensional data.
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CausalMix: Data Mixture as Causal Inference for Language Model Training
CausalMix fits a causal model on 512 runs of a 0.5B model to estimate CATE, then extrapolates optimal mixtures for an 800K data pool applied to 7B and 4B models, outperforming RegMix.
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To Use AI as Dice of Possibilities with Timing Computation
The paper defines possibility space, timing computation, and causal factum to make timing a computable variable, and illustrates the framework with automatic trajectory discovery and counterfactual timing on 3,276 breast-cancer patients.