What is the Causal Effect of a Conversation? Estimands and Inference in AI Mediated Conversations

Abstract: Political scientists increasingly use conversations as treatments. Sometimes these conversations are conducted by humans, but often and increasingly they will be conducted by generative 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.

• The paper presents a potential outcomes framework that disambiguates various causal estimands in dynamic, AI-mediated conversations.
• It outlines experimental strategies such as sequential randomization and instrumental variable techniques to address endogenous conversational features.
• The study highlights identification challenges including path endogeneity, feature bundling, and sequential dependencies, urging rigorous experimental design.

Causal Inference in AI-Mediated Conversations: Estimands, Identification, and Design

Introduction

The analysis of conversational treatments in political science is increasingly intertwined with AI-mediated environments. The paper "What is the Causal Effect of a Conversation? Estimands and Inference in AI Mediated Conversations" (2607.03597) offers a rigorous potential outcomes framework tailored to the study of conversations as dynamic treatments. Recognizing that conversations are co-produced sequences rather than static exposures, the authors delineate a spectrum of causal estimands relevant to AI-mediated and human-human interactions, cataloging the identification challenges that arise from the endogenous and sequential nature of conversational data. The framework is situated at the intersection of causal inference with text, dynamic treatment regimes, and the empirical analysis of multi-agent dialogue, with strong implications for the design of experiments, interpretation of estimands, and the applicability of standard identifying assumptions.

Causal Objects and Estimands in Conversational Settings

This work emphasizes that conversations, whether between humans or with AI, are not fixed or passively received; their content, features, and trajectories are actively produced by both parties. The central contribution is to disambiguate multiple causal objects:

• Assignment Effects: The intent-to-treat (ITT) effect of being assigned to a conversational condition (e.g., a policy or prompt).
• Opening Message Effects: The effect of the initial message, as opposed to the broad policy or general condition.
• Conversational Policy Effects: The effect of varying the rules, prompts, or model configurations governing the AI or human interlocutor.
• Message Effects (Conditional on History): The local effect of a specific message, controlling for prior conversational history.
• Full Path/Conversation Effects: The effect of experiencing a particular realized conversation trajectory.
• Feature and Dosage Effects: Effects defined on latent or aggregated features of conversation (e.g., incivility, empathy), including cumulative exposure.

The paper formally defines the potential outcomes and average treatment effects (ATEs) associated with each, providing detailed notation and causal diagrams that clarify the relationships between assignment, policies, histories, messages, features, and outcomes.

Identification Challenges in Dynamic, Co-Produced Treatments

The principal challenge addressed is that standard randomization over assignment ($Z_i$) does not in general identify the effect of the realized conversation or its component features. The realized trajectory $C_i$—and thus the conversational features $D_i = f(C_i)$—are endogenous to respondent characteristics $U_i$, such as beliefs, engagement, or communicative tendencies, which may also affect the outcome $Y_i$. As a result, comparisons across realized conversations or features are confounded by joint respondent-agent generation.

The authors enumerate the following identification failures:

• Path and Feature Endogeneity: Realized conversational exposure reflects both treatment and respondent traits, violating ignorability.
• Feature Bundling: Latent conversational attributes of interest (e.g., civility) co-vary with unmeasured features, complicating interpretation.
• Sequential Assignment: The effect of a message is conditional on the endogenous conversational history, often unique in high-dimensional settings, impeding empirical identification.

Propositions explicate the bias decomposition when estimating message or feature effects from observational or even randomized assignment data, showing the inescapable confounding unless additional design or modeling strategies are used.

Strategies for Causal Identification: Experimental Design Implications

The framework informs several experiment design strategies:

1. Align Interventions with Causal Targets: Randomization should be as proximal as possible to the object of theoretical inferential interest (e.g., direct randomization of opening messages, policies, or messages at specific turns).
2. Sequential and Conditional Randomization: Designs inspired by SMART and dynamic treatment regimes introduce randomization at decision points within a conversation, with identification valid within histories where such randomization occurs.
3. Constrained Protocols and Policy Leverage: Restricting the action space of interlocutors or employing policies that “strongly” shape the conversation can clarify exposure; however, interpretation is tethered to the strength and content of the constraint.
4. Instrumental Variable Approaches: When direct manipulation is infeasible, assignment can serve as an instrument for features (e.g., using random AI tone assignments to estimate LATEs for incivility), but exclusion and monotonicity requirements are severe.
5. Representation-Based Conditioning: Conditioning on lower-dimensional representations of conversational history (e.g., latent embeddings or summary statistics) enables context-specific CATEs, but residual confounding persists if representations are not sufficient for outcome and message assignment.

Extension to Human Interlocutors and Further Complications

While the framework is motivated by AI-mediated conversations, it is directly relevant to human-human experiments. In human interactions, additional complications arise: interlocutor identity and variation, interpretation of latent cues, nonverbal signals, and cross-unit spillovers (e.g., learning across interactions) further obfuscate causal identification. Therefore, the enumerated challenges are not artifacts of AI but fundamental to conversational inference.

Implications and Perspectives for Future AI Research

The theoretical and methodological insights provided have implications that extend to the design and analysis of AI-based social interventions, large-scale LLM-powered dialogue studies, and the modeling of policy effects in dynamic, interactive systems. The main practical consequence is that experimentalists must be explicit about their causal objects and remain cautious when interpreting ITT estimates as effects on exposure to specific message features or conversation-level attributes. Future work should focus on empirically tractable representations of conversational context, develop sequential randomization and instrumental designs, and extend the framework to multi-party and multimodal conversational domains. Enhanced AI and computational methods for dialog abstraction and representation learning may facilitate better conditioning, but will not, by themselves, dissolve the necessity for strong identifying assumptions or the challenges posed by jointly-produced treatments.

Conclusion

This work brings necessary conceptual clarity and formal rigor to the estimation and interpretation of causal effects in AI-mediated and human conversations. By mapping the distinction between assignment, policies, messages, features, and realized conversational paths onto a hierarchy of estimands and assumptions, it renders visible the challenges inherent to dynamic, interactive treatments. The framework crucially establishes that while some effects are amenable to standard experimental manipulation and inference, those aligned with richer substantive theories, especially those involving latent conversational features, require more ambitious designs, careful theoretic interpretation, and humility regarding identification claims. The adaptation of this framework is central for the advancement of empirical research into AI-mediated persuasion, deliberation, and social influence, and for the principled deployment of interactive AI systems in real-world social science and policy evaluation.