Causal State-Space Model for Causal Inference: Estimating Longitudinal Individual Treatment Effects
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...
| Authors | Abisoye Abidakun, Mingjun Zhong, Georgios Leontidis |
|---|---|
| Primary category | cs.LG |
| Published | 8 August 2026 |
| Last updated at source | 12 August 2026 |
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