causal inference

Methods and reasoning used to determine whether an observed association between variables represents a genuine cause-effect relationship rather than mere correlation, which is essential for using prediction market prices to evaluate policy or candidate impacts.

Cluster: Information Theory

Related Concepts

• quasi-experimental design — Empirical approaches that estimate causal effects using natural variation in treatment assignment without random controlled experiments; techniques like regression discontinuity and difference-in-differences exploit built-in thresholds or exogenous shocks to approximate the counterfactual, and can be paired with prediction markets to evaluate candidate or policy impacts.
• election markets — Prediction markets focused on political elections, often the highest-volume and most visible markets.
• futarchy — A governance system where policy decisions are made based on which option prediction markets forecast will produce the best outcomes.

Articles about causal inference

• It's Sometimes Sunny in Philadelphia by Bolton