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.
In: UAI Workshop on Causal Structure Learning (2012)
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
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.
citing papers explorer
-
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.
-
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.