发现因果图中估计与预测存在权衡，高精度参数估计导致预测不确定性上升。
Estimation-Prediction Tradeoff in Causal Probabilistic Temporal Graphs
- 基于二元逻辑模型与费舍尔信息熵的单调关系，揭示估计-预测权衡机制。
- 在真实因果结构下，高费舍尔信息区的预测熵更高，不可约误差增大。
- 适用于研究时序图因果机制的学者，尤其关注参数可解释性与预测性能者。
时序边预测（TLP）通常通过未见边的预测性能评估，但这可能混淆预测准确性与对底层因果机制的恢复。在随机模型中，费舍尔信息决定了参数估计误差的克拉默-拉奥（CR）界：费舍尔信息越高，参数恢复越准确。我们证明，在费舍尔信息与熵呈同向变化条件下，二元逻辑模型表现出估计-预测权衡：费舍尔信息更高的区域，其CR界更小，但不可约预测熵也更高。为研究这一现象，我们引入一个具有瞬时边和已知真实因果结构的概率因果生成器，并实证验证了该权衡现象。
原文摘要 · Abstract (English)
Temporal link prediction (TLP) is typically evaluated by predictive performance on unseen edges, but this criterion can conflate predictive accuracy with recovery of the underlying causal mechanism. In stochastic models, Fisher information governs the Cramér--Rao (CR) bound on parameter estimation error: higher Fisher information permits more accurate parameter recovery. We show that, under comonotonicity conditions between Fisher information and entropy, binary logistic models exhibit an estimation--prediction tradeoff: regimes with higher Fisher information, and hence smaller CR bounds, also have higher irreducible predictive entropy. To study this tradeoff in TLP, we introduce a probabilistic causal generator for temporal graphs with transient edges and known ground-truth causal structure, and validate the phenomenon empirically.
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