Quantum Causality: Resolving Simpson's Paradox with $\mathcal{DO}$-Calculus
This work addresses the problem of causal inference for machine intelligence, offering a new computational tool for algorithmic fairness and explainable AI, though it is incremental as it builds on existing causal frameworks.
The paper tackled the challenge of implementing causal inference by developing a quantum algorithmic framework that maps causal networks to quantum circuits, experimentally validating it on a quantum computer to resolve Simpson's Paradox and quantify confounding bias in simulations.
Distinguishing correlation from causation is a fundamental challenge in machine intelligence, often representing a critical barrier to building robust and trustworthy systems. While Pearl's $\mathcal{DO}$-calculus provides a rigorous framework for causal inference, a parallel challenge lies in its physical implementation. Here, we apply and experimentally validate a quantum algorithmic framework for performing causal interventions. Our approach maps causal networks onto quantum circuits where probabilistic links are encoded by controlled-rotation gates, and interventions are realized by a structural remodeling of the circuit -- a physical analogue to Pearl's ``graph surgery''. We demonstrate the method's efficacy by resolving Simpson's Paradox in a 3-qubit model, and show its scalability by quantifying confounding bias in a 10-qubit healthcare simulation. Critically, we provide a proof-of-principle experimental validation on an IonQ Aria quantum computer, successfully reproducing the paradox and its resolution in the presence of real-world noise. This work establishes a practical pathway for quantum causal inference, offering a new computational tool to address deep-rooted challenges in algorithmic fairness and explainable AI (XAI).