This research introduces HECATE, a framework that integrates encoder-decoder architectures with causal reasoning for robust multi-step causal inference. It addresses limitations in long-horizon predictions, distributional shifts, and confounding bias through novel regularization, invariant representation learning, and attention-based deconfounding.
Key findings
HECATE combines encoder-decoder architectures with structured causal reasoning.
Dual-pathway encoder processes confounders and intervention histories separately.
Neural structural transition module enforces constraints from Pearl’s causal hierarchy.
Multi-horizon decoder includes causal consistency regularization to reduce error accumulation.
Adversarial training promotes distributional robustness across intervention sequences.
Limitations & open questions
The proposed method requires comprehensive validation across various datasets.
The approach's scalability and real-world applicability need further exploration.