Search papers, labs, and topics across Lattice.
3
0
3
0
Time series causal discovery, a field spanning over 50 years, is now a critical component for AI systems and scientific discovery, yet remains fragmented across disciplines.
Amortized Bayesian causal inference for hidden confounding is now possible via a single transformer pass, achieving state-of-the-art CATE estimation without hyperparameter tuning.
Temporal dependence doesn't have to compromise the validity of causal inference鈥擠R-ACI ensures reliable prediction intervals even in complex settings.
