Statistical Foundations of LLM-based A/B Testing: A Surrogacy Framework for Human Causal Inference
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基于大语言模型的 A/B 测试：人类因果推断的替代终点框架
- 研究问题
- 用大语言模型替代人类参与 A/B 测试时，哪些条件能使模型结果中的处理效应恢复目标人群的平均处理效应？
- 方法与思路
- 作者将替代终点理论用于模型输出，通过人类结果校准建立识别框架，提出替代性证伪检验、重叠不足的偏差界和重复生成平均策略。
- 材料陈述的贡献
- 摘要称校准在替代性与可比性条件下识别平均处理效应；应用中原始输出仅恢复人类效应的 39%，非参数校准缩小了差距。
- 阅读关注点
- 应核对替代性与可比性的正式定义、检验能排除哪些违背情形，以及人类试点、样本重叠和重复生成次数如何影响推断。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收均未核实；摘要中的校准效果来自特定应用，不能据此保证跨模型、提示或人群有效。
原文摘要
Organizations and researchers show increasing interest in using large language models (LLMs) in place of human participants in A/B tests, in the hope of experimenting faster and at lower cost. We study when a treatment effect estimated on LLM outcomes can recover the effect for the human population of interest. Distributional equivalence between LLM and human outcomes would make any standard estimator valid but is unrealistic. We therefore develop a statistical framework that adapts surrogate endpoint theory to LLMs, showing that calibrating LLM outcomes to human outcomes identifies the average treatment effect under surrogacy and comparability conditions that are jointly weaker than distributional equivalence. We present a falsification test for surrogacy and a bound on the worst-case bias from limited overlap between the LLM and human samples. We further show that the stochasticity inherent to LLMs can weaken surrogacy for identification while also introducing bias and variance during estimation, but that using an average over multiple LLM draws per unit as the surrogate mitigates these issues. Simulations validate the results, and an empirical application to the Upworthy Research Archive dataset shows that raw LLM outputs recover only 39% of the human treatment effect while nonparametric calibration closes the gap. A central takeaway is that A/B testing on LLM responses is correct only by assumption, whereas A/B testing on humans is correct by design, and that the required assumptions are hardest to justify precisely where LLMs promise the greatest benefit. We discuss the choice of LLM, prompting, and temperature as design variables, the compounded challenge posed by long-term outcomes, and how to size human pilot studies for validation.