主动因果实验者 (ACE): 通过直接偏好优化学习干预策略
Active Causal Experimentalist (ACE): Learning Intervention Strategies via Direct Preference Optimization
AI总结：
提出ACE框架，将实验设计建模为顺序策略，利用直接偏好优化从干预对比较中学习，在合成、物理和经济数据上以等干预预算取得70-71%的提升，并自主发现碰撞机制需集中干预父变量的理论策略。
摘要：
发现因果关系需要受控实验，但实验者面临一个顺序决策问题：每次干预都会揭示信息，这些信息应指导下一步尝试。传统方法如随机采样、贪婪信息最大化和轮询覆盖将每个决策孤立处理，无法从经验中学习自适应策略。我们提出主动因果实验者（ACE），它将实验设计作为顺序策略学习。我们的关键见解是，虽然绝对信息增益随着知识积累而减少（使得基于价值的强化学习不稳定），但候选干预之间的相对比较在整个过程中仍然有意义。ACE通过直接偏好优化利用这一点，从成对干预比较而非非平稳奖励幅度中学习。在合成基准、物理模拟和经济数据上，ACE在相等干预预算下比基线提高了70-71%（p < 0.001，Cohen's d ~ 2）。值得注意的是，学习到的策略自主发现碰撞机制需要对父变量进行集中干预，这是一种纯粹从经验中涌现的理论基础策略。这表明基于偏好的学习可以恢复有原则的实验策略，用学习到的领域适应补充理论。
英文摘要：
Discovering causal relationships requires controlled experiments, but experimentalists face a sequential decision problem: each intervention reveals information that should inform what to try next. Traditional approaches such as random sampling, greedy information maximization, and round-robin coverage treat each decision in isolation, unable to learn adaptive strategies from experience. We propose Active Causal Experimentalist (ACE), which learns experimental design as a sequential policy. Our key insight is that while absolute information gains diminish as knowledge accumulates (making value-based RL unstable), relative comparisons between candidate interventions remain meaningful throughout. ACE exploits this via Direct Preference Optimization, learning from pairwise intervention comparisons rather than non-stationary reward magnitudes. Across synthetic benchmarks, physics simulations, and economic data, ACE achieves 70-71% improvement over baselines at equal intervention budgets (p < 0.001, Cohen's d ~ 2). Notably, the learned policy autonomously discovers that collider mechanisms require concentrated interventions on parent variables, a theoretically-grounded strategy that emerges purely from experience. This suggests preference-based learning can recover principled experimental strategies, complementing theory with learned domain adaptation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
