解决纵向数据因果推断中平衡表示与预测精度的矛盾，提升治疗效果估计准确率。
Causal State-Space Model for Causal Inference: Estimating Longitudinal Individual Treatment Effects
- 提出两类状态空间模型，通过并行多步解码消除滚动误差
- 在MIMIC-III上6步预测RMSE降低0.07，癌症模拟下最优平均降低12.7%
- 首次形式化信息冲突，适合医疗决策支持研究者使用
从纵向观测数据中估计时序反事实结果是临床决策支持的核心。现有方法依赖领域混淆——通过对抗训练使表征对治疗分配不变——但这种不变性引发互信息冲突：抑制了与治疗相关的协变量信号，影响结果预测准确性。本文通过杰恩森-香农散度界形式化这一张力，并提出两种互补模型。CSSD（带直接解码器的因果状态空间模型）采用选择性状态空间模型与并行多步解码器，在单次前向传播中同时输出所有预测时步，消除累积滚动误差。CSSPD在CSSD基础上引入对比预测编码与局部信息最大化，强化平衡表征中的时序可预测性，并恢复被领域混淆破坏的局部协变量信息。在MIMIC-III数据集上，当预测时步τ≥2时，CSSPD在所有时步的反事实RMSE均低于因果Transformer，且代价为O(T)编码成本，6步预测提升达0.07；在不同混杂强度γ∈{0,1,2,3,4}的癌症模拟实验中，γ≤3时性能优于因果Transformer（提升25.9%–37.0%），而CSSD整体平均RMSE最低，较因果Transformer降低12.7%。据我们所知，这是首个形式化平衡-预测互信息冲突并提出结构化解法的工作。
原文摘要 · Abstract (English)
Estimating counterfactual outcomes over time from longitudinal observational data is central to clinical decision support. Existing methods rely on domain confusion -- adversarial training that renders representations invariant to treatment assignment -- yet this invariance creates a mutual information conflict: it suppresses treatment-correlated covariate signals necessary for accurate outcome prediction. We formalise this tension via a Jensen-Shannon divergence bound on counterfactual prediction error and develop two complementary models. CSSD (Causal State-Space model with Direct decoder) adapts selective State Space Models with a parallel multi-step decoder that eliminates accumulated rollout error by producing all prediction horizons simultaneously in a single forward pass. CSSPD (Causal State-Space model with Predictive regularisation and Direct decoder) augments CSSD with Contrastive Predictive Coding and Local Information Maximisation to reinforce temporal predictability in the balancing representation and recover local covariate information destroyed by domain confusion. On MIMIC-III, CSSPD achieves lower counterfactual RMSE than the Causal Transformer at every horizon tau >= 2 at O(T) encoder cost, with gains from 0.02 (2-step) to 0.07 (6-step). On Cancer Simulation across confounding strengths gamma in {0,1,2,3,4}, CSSPD outperforms CT at gamma <= 3 (margins 25.9%--37.0%), and CSSD achieves the lowest overall average RMSE (12.7% reduction over CT), confirming the MI conflict analysis. To our knowledge, this is the first work to formalise the balancing-prediction MI conflict and propose a structured resolution through complementary predictive and information-theoretic training objectives.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。