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

TL;DR

This paper introduces a causal counterfactual framework for social simulations, enabling more accurate policy evaluation by distinguishing necessity and sufficiency of interventions.

Contribution

It formalizes causal semantics in social simulations, linking simulation design to explicit causal assumptions for policy-relevant insights.

Findings

Proposes adopting counterfactual causal semantics in social simulations.

Distinguishes between necessary and sufficient causation for policy analysis.

Highlights the importance of simulation fidelity for causal inference.

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.