LTTS: A Predictor-Corrector for Long-Horizon Counterfactual Outcome Prediction

Comparing treatment plans requires predicting how outcomes evolve beyond a common observed history. Recursive models construct these trajectories by using generated states as inputs to later predictions. Without new measurements, errors in these inputs can affect the remaining continuation. To address this problem, we propose Long-term Trajectory Stabilization (LTTS), a predictor–corrector for long-horizon outcome prediction under sustained treatment and control from the same observed prefix. We use the predictor (TSA) to advance each treatment-specific trajectory, while the adaptation module (TAM) uses historical reconstruction to adjust selected generated states before they are reused. Using a frozen monitor, we trigger limited private adaptation on the current generated block, retaining the shared reconstruction modules without using new outcome labels. The updated window supports the next prediction; prescribed treatments and reported outcomes are preserved. Across two synthetic benchmarks and a MIMIC-IV-derived Cox model-recovery benchmark, we obtain 4.2–32.6% lower mean NMAE than the best evaluated external baseline per setting in 150-step evaluations over 30 seeds. With the predictor held fixed, we observe lower mean outcome error with feedback. Subsequent predictions also improve after a single correction when further corrections are disabled. Together, these results support combining trajectory prediction with selective state feedback to improve long-horizon outcome prediction under the evaluated treatment continuations.