Motivation

Standard time-series forecasting models learn correlations, not causes. This matters when interventions change the data-generating process — a model trained on observational data will fail precisely when you need it, i.e., when you act. The goal here is to integrate structural causal models (SCMs) with modern state-space architectures.

Approach

I augment SSM architectures (S4, Mamba) with a causal graph layer that encodes the structural equations governing the latent state transitions. The causal graph is estimated from data using PC algorithm / NOTEARS, then used to constrain the transition matrix of the SSM.

Formally, given a structural equation:

$$X_t = f(PA(X_t), \epsilon_t)$$

the intervention distribution under $do(X_j = x)$ is estimated by graph surgery in the latent space before decoding to the observed series.

Baselines

  • Granger causality VAR models
  • Standard Mamba without causal constraints
  • PCMCI+ for causal discovery
  • CausalImpact (Google) for intervention estimation

Datasets

Evaluating on: M4 financial series (observational), MIMIC-III vitals (clinical interventions), ERA5 climate reanalysis (physical causal structure known). Counterfactual evaluation via synthetic interventions with known ground truth.

Status

Ongoing. Causal discovery pipeline complete. SSM integration in progress.

← Back to Projects