A Tabu-based algorithm learns time-ordered causal graphs from time series by optimizing per-edge lags with a decomposable BIC score and explicit lag penalty.
The annals of statistics, 461– 464 (1978)
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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.
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Time series causal discovery with variable lags
A Tabu-based algorithm learns time-ordered causal graphs from time series by optimizing per-edge lags with a decomposable BIC score and explicit lag penalty.
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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.