Causal Intelligence for Constraint-Aware Intervention Design to Induce State Transitions

2026-05-27

AI Summary

This work addresses the core challenge of designing feasible and robust interventions that drive state transitions in complex systems. It proposes COAST, a novel framework that uniquely integrates mechanism-level causal discovery with constraint-aware multi-objective optimization. By learning context-specific causal graphs and structural causal models, COAST identifies key causal drivers and balances trade-offs among intervention efficacy, complexity, and target stability. The approach offers a transparent, modular, and domain-agnostic end-to-end paradigm for generating both single- and multi-target intervention strategies. Experiments on synthetic and real biological data demonstrate that COAST successfully recovers known causal mechanisms and produces efficient, interpretable, and experimentally verifiable intervention policies.

Abstract

Driving a system from one state to another through targeted interventions is a fundamental challenge in science, yet most predictive models offer limited mechanistic insight and no principled framework for decision-making. Here we present COAST (Causally Optimal Actions for State Transitions), a causal-intelligence approach for the in-silico design of constrained interventions that induce user-defined state transitions. Given data characterizing source and target states, COAST learns context-specific causal graphs and structural causal models, attributes observed distributional shifts to mechanism-level causal drivers, and introduces a novel constraint-aware multi-objective optimization formulation that balances transition efficacy, intervention complexity, and target-state stability. The approach is modular and domain-agnostic, integrating feature selection, causal discovery, causal modeling, and intervention identification and evaluation through interchangeable components. Across synthetic benchmarks and real biological datasets, COAST recovers key causal drivers and identifies robust single- and multi-target intervention strategies that achieve desired state transitions, accompanied by transparent mechanistic rationales to guide experimental validation.

Problem

• causal intelligence
• constraint-aware intervention
• state transitions
• intervention design
• causal discovery

Innovation

• causal intelligence
• constraint-aware optimization
• state transitions
• structural causal models
• intervention design