AINeutralarXiv – CS AI · Mar 34/107
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Econometric vs. Causal Structure-Learning for Time-Series Policy Decisions: Evidence from the UK COVID-19 Policies
A research study compares econometric methods versus causal machine learning algorithms for analyzing time-series data to inform policy decisions, using UK COVID-19 policies as a case study. The research evaluates four econometric methods against eleven causal ML algorithms, finding that econometric methods provide clearer temporal structure rules while causal ML algorithms explore broader graph structures to capture more causal relationships.