From Plausible to Causal: Counterfactual Semantics for Policy Evaluation in Simulated Online Communities
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 ...
| Authors | Agam Goyal, Yian Wang, Eshwar Chandrasekharan et al. |
|---|---|
| Primary category | cs.CL |
| Published | 5 April 2026 |
| Last updated at source | 15 April 2026 |
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