世界モデルのプラグインとしてのCausalVAE：信頼できる反事実ダイナミクスに向けて
CausalVAE as a Plug-in for World Models: Towards Reliable Counterfactual Dynamics
潜在世界モデルに因果構造を組み込むプラグインモジュールCausalVAEを提案し、反事実的推論の精度を大幅に向上させた。
著者: Ziyi Ding, Xianxin Lai, Weiyu Chen, Xiao-Ping Zhang, Jiayu Chen
分類: cs.LG
原文アブストラクト
In this work, CausalVAE is introduced as a plug-in structural module for latent world models and is attached to diverse encoder-transition backbones. Across the reported benchmarks, competitive factual prediction is preserved and intervention-aware counterfactual retrieval is improved after the plug-in is added, suggesting stronger robustness under distribution shift and interventions. The largest gains are observed on the Physics benchmark: when averaged over 8 paired baselines, CF-H@1 is improved by +102.5%. In a representative GNN-NLL setting on Physics, CF-H@1 is increased from 11.0 to 41.0 (+272.7%). Through causal analysis, learned structural dependencies are shown to recover meaningful first-order physical interaction trends, supporting the interpretability of the learned latent causal structure.