Counterfactual Outcome Prediction using Structured State Space Model
Vishal Purohit
Counterfactual outcome prediction in longitudinal data has recently gained
attention due to its potential applications in healthcare and social sciences.
In this paper, we explore the use of the state space model, a popular sequence
model, for this task. Specifically, we compare the performance of two models:
Treatment Effect Neural Controlled Differential Equation (TE-CDE) and
structured state space model (S4Model). While TE-CDE uses controlled
differential equations to address time-dependent confounding, it suffers from
optimization issues and slow training. In contrast, S4Model is more efficient
at modeling long-range dependencies and easier to train. We evaluate the models
on a simulated lung tumor growth dataset and find that S4Model outperforms
TE-CDE with 1.63x reduction in per epoch training time and 10x better
normalized mean squared error. Additionally, S4Model is more stable during
training and less sensitive to weight initialization than TE-CDE. Our results
suggest that the state space model may be a promising approach for
counterfactual outcome prediction in longitudinal data, with S4Model offering a
more efficient and effective alternative to TE-CDE.