Controllable Sequence Editing for Counterfactual Generation
Journal:
arXiv
Published Date:
Feb 5, 2025
Abstract
Sequence models generate counterfactuals by modifying parts of a sequence
based on a given condition, enabling reasoning about "what if" scenarios. While
these models excel at conditional generation, they lack fine-grained control
over when and where edits occur. Existing approaches either focus on univariate
sequences or assume that interventions affect the entire sequence globally.
However, many applications require precise, localized modifications, where
interventions take effect only after a specified time and impact only a subset
of co-occurring variables. We introduce CLEF, a controllable sequence editing
model for counterfactual reasoning about both immediate and delayed effects.
CLEF learns temporal concepts that encode how and when interventions should
influence a sequence. With these concepts, CLEF selectively edits relevant time
steps while preserving unaffected portions of the sequence. We evaluate CLEF on
cellular and patient trajectory datasets, where gene regulation affects only
certain genes at specific time steps, or medical interventions alter only a
subset of lab measurements. CLEF improves immediate sequence editing by up to
36.01% in MAE compared to baselines. Unlike prior methods, CLEF enables
one-step generation of counterfactual sequences at any future time step,
outperforming baselines by up to 65.71% in MAE. A case study on patients with
type 1 diabetes mellitus shows that CLEF identifies clinical interventions that
shift patient trajectories toward healthier outcomes.
