Econometric methods impose clear temporal rules on causal structures from time series, whereas causal ML algorithms produce denser graphs that recover more identifiable causal effects in UK COVID-19 policy data.
Journal of the Royal Statistical Society: Series B (Methodological)41(2), 190–195 (1979)
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Econometric vs. Causal Structure-Learning for Time-Series Policy Decisions: Evidence from the UK COVID-19 Policies
Econometric methods impose clear temporal rules on causal structures from time series, whereas causal ML algorithms produce denser graphs that recover more identifiable causal effects in UK COVID-19 policy data.