TY - GEN

T1 - Causal Forecasting

T2 - 38th Conference on Uncertainty in Artificial Intelligence, UAI 2022

AU - Vankadara, Leena Chennuru

AU - Faller, Philipp Michael

AU - Hardt, Michaela

AU - Minorics, Lenon

AU - Ghoshdastidar, Debarghya

AU - Janzing, Dominik

N1 - Publisher Copyright:
© 2022 Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence, UAI 2022. All right reserved.

PY - 2022

Y1 - 2022

N2 - 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 causal risk. Here, we study the problem of 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 statistical associations compare with its ability to predict under interventions? To this end, we introduce the framework of causal learning theory for forecasting. Using this framework, we obtain a characterization of the difference between statistical and causal risks, which helps identify sources of divergence between them. Under causal sufficiency, the problem of causal generalization amounts to learning under covariate shifts albeit with additional structure (restriction to interventional distributions under the VAR model). This structure allows us to obtain uniform convergence bounds on causal generalizability for the class of VAR models. To the best of our knowledge, this is the first work that provides theoretical guarantees for causal generalization in the time-series setting.

AB - 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 causal risk. Here, we study the problem of 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 statistical associations compare with its ability to predict under interventions? To this end, we introduce the framework of causal learning theory for forecasting. Using this framework, we obtain a characterization of the difference between statistical and causal risks, which helps identify sources of divergence between them. Under causal sufficiency, the problem of causal generalization amounts to learning under covariate shifts albeit with additional structure (restriction to interventional distributions under the VAR model). This structure allows us to obtain uniform convergence bounds on causal generalizability for the class of VAR models. To the best of our knowledge, this is the first work that provides theoretical guarantees for causal generalization in the time-series setting.

UR - http://www.scopus.com/inward/record.url?scp=85146143315&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:85146143315

T3 - Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence, UAI 2022

SP - 2002

EP - 2012

BT - Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence, UAI 2022

PB - Association For Uncertainty in Artificial Intelligence (AUAI)

Y2 - 1 August 2022 through 5 August 2022

ER -