Causal Forecasting:Generalization Bounds for Autoregressive Models. (arXiv:2111.09831v2 [stat.ML] UPDATED)

Despite the increasing relevance of forecasting methods, causal implications
of these algorithms remain largely unexplored. This is concerning considering
that, even under simplifying assumptions such as causal sufficiency, the
statistical risk of a model can differ significantly from its \textit{causal
risk}. Here, we study the problem of \textit{causal generalization} --
generalizing from the observational to interventional distributions -- in
forecasting. Our goal is to find answers to the question: How does the efficacy
of an autoregressive (VAR) model in predicting …

arxiv autoregressive models forecasting