From Plausible to Causal: Counterfactual Semantics for Policy Evaluation in Simulated Online Communities

2026-04-04

AI Summary

While existing large language model–based social simulations can generate realistic interactions, they lack causal semantics, limiting their ability to support reliable causal inference for governance interventions. This work introduces, for the first time, a systematic integration of necessity and sufficiency–based causal concepts into social simulation, establishing a counterfactual framework tailored for policy evaluation. The framework explicitly articulates the relationship between simulator fidelity and policy relevance, offering theoretical guidance for simulator design and defining the fidelity criteria necessary to support valid policy inferences. By doing so, it advances social simulation beyond mere plausibility toward genuine decision-support capability.

Abstract

LLM-based social simulations can generate believable community interactions, enabling ``policy wind tunnels'' where governance interventions are tested before deployment. But believability is not causality. Claims like ``intervention $A$ reduces escalation'' require causal semantics that current simulation work typically does not specify. We propose adopting the causal counterfactual framework, distinguishing \textit{necessary causation} (would the outcome have occurred without the intervention?) from \textit{sufficient causation} (does the intervention reliably produce the outcome?). This distinction maps onto different stakeholder needs: moderators diagnosing incidents require evidence about necessity, while platform designers choosing policies require evidence about sufficiency. We formalize this mapping, show how simulation design can support estimation under explicit assumptions, and argue that the resulting quantities should be interpreted as simulator-conditional causal estimates whose policy relevance depends on simulator fidelity. Establishing this framework now is essential: it helps define what adequate fidelity means and moves the field from simulations that look realistic toward simulations that can support policy changes.

Problem

• causal inference
• counterfactual semantics
• policy evaluation
• social simulation
• LLM-based simulation

Innovation

• counterfactual semantics
• causal inference
• policy evaluation
• social simulation
• LLM-based simulation