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.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.