arXiv:2605.29008v1 Announce Type: new 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, at
The proliferation of advanced AI models and growing complexity of interconnected systems necessitate more sophisticated decision-making frameworks for state transitions.
This research provides a principled method for designing targeted interventions based on causal intelligence, moving beyond mere predictive models to actionable strategic control over complex systems.
The ability to induce user-defined state transitions in complex systems with 'causally optimal actions' transforms how AI can be applied to scientific research, engineering, and potentially societal challenges.
- · AI researchers
- · Automation companies
- · Biotech and drug discovery
- · Complex system operators
- · Trial-and-error methodologies
- · Systems lacking definable states
Improved efficiency and precision in scientific experimentation and engineering processes through AI-driven intervention design.
Accelerated discovery of new materials, biological pathways, or system configurations by proactively guiding systems to desired states.
Enhanced resilience and adaptability of critical infrastructure through AI that can autonomously design interventions to prevent or mitigate undesirable state transitions.
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Read at arXiv cs.LG