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 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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search
Learning policies on data synthesized by models can in principle quench the thirst of reinforcement learning algorithms for large amounts of real experience, which is often costly to acquire. However, simulating plausibl…
counterfactualReinforcement LearningBounding probabilities of causation through the causal marginal problem
Probabilities of Causation play a fundamental role in decision making in law, health care and public policy. Nevertheless, their point identification is challenging, requiring strong assumptions such as monotonicity. In …
counterfactualDecision MakingCausal Counterfactuals Reconsidered
I develop a novel semantics for probabilities of counterfactuals that generalizes the standard Pearlian semantics: it applies to probabilistic causal models that cannot be extended into realistic structural causal models…
Benchmarking Counterfactual Prediction in Epidemic Time Series with Time-Varying Interventions
Deep learning has enabled significant advances in time-series causal inference, yet progress remains constrained by the lack of realistic benchmarks with observable counterfactual outcomes. Existing datasets either rely …
Causal InferenceCounterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models
We introduce an off-policy evaluation procedure for highlighting episodes where applying a reinforcement learned (RL) policy is likely to have produced a substantially different outcome than the observed policy. In parti…
counterfactualManagementOff-policy evaluation