What is the Causal Effect of a Conversation? Estimands and Inference in AI Mediated Conversations
对话的因果效应是什么？AI 中介对话的目标量与推断AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确采用潜在结果框架区分随机分配与内生对话内容的因果效应，并讨论相应识别设计，因此归入因果推断。
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对话的因果效应是什么？AI 中介对话的目标量与推断
- 研究问题
- 参与者被随机分配至对话条件后，对话内容仍由双方共同生成；如何区分分配效应、消息效应与实际完整对话的因果效应？
- 方法与思路
- 作者建立对话的潜在结果框架，区分条件、策略、开场消息、会话消息和实际特征等目标，并讨论随机设计及额外识别要求。
- 材料陈述的贡献
- 摘要称，框架为对话实验提供统一目标语言，明确标准随机设计能识别哪些量，以及何时需要序贯假设或消息层面的干预设计。
- 阅读关注点
- 应核对各目标量的干预定义、对话历史表示及识别条件，尤其注意随机分配对话条件为何不足以识别内生生成的实际特征效应。
- 阅读边界
- 所给版本为 arXiv 预印本，不能据此认定已同行评审或获期刊接收；摘要未给出完整识别公式、估计程序或经验验证结果。
原文摘要
Political scientists increasingly use conversations as treatments. Sometimes these conversations are conducted by humans, but often and increasingly they will be conducted by generative artificial intelligence (AI). AI makes it possible to scale treatments that are responsive, and rich in real-world relevance. But this same interactivity creates specific challenges for causal inference. A respondent may be randomly assigned to a conversational condition, but the conversation that follows is not merely received by the respondent. It is generated jointly by the respondent and the conversational agent and is thus endogenous to who the respondent is. Consequently, when the theoretical object of interest is the conversation itself -- or particular messages, features, or other attributes of that conversation -- randomization of assignment does not necessarily identify the causal quantity the researcher seeks to estimate. This paper develops a potential outcomes framework for causal inference with conversations, with broad application but particular relevance to AI-mediated interaction. We distinguish among several causal objects: assignment to a conversational condition, assignment to a conversational policy, opening messages, messages within a conversation, realized conversational features, and the full realized conversation. Each corresponds to a distinct estimand and a different set of identifying assumptions. While some of these quantities are identified by standard randomized designs, others require additional assumptions or research designs, including sequential assumptions, representations of conversational histories, or explicit message-level interventions. The framework clarifies these distinctions and provides a common language for defining, interpreting, and designing conversational experiments as conversations increasingly become objects of causal inquiry.