Causal Discovery from Temporally Aggregated Time Series

Mingming Gong, Kun Zhang, Bernhard Schölkopf, Clark Glymour, Dacheng Tao
Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence, PMLR R15:161-170, 2017.

Abstract

Discovering causal structure of a dynamical sys- tem from observed time series is a traditional and important problem. In many practical ap- plications, observed data are obtained by apply- ing subsampling or temporally aggregation to the original causal processes, making it difficult to discover the underlying causal relations. Subsam- pling refers to the procedure that for every k con- secutive observations, one is kept, the rest being skipped, and recently some advances have been made in causal discovery from such data. With temporal aggregation, the local averages or sums of k consecutive, non-overlapping observations in the causal process are computed as new obser- vations, and causal discovery from such data is even harder. In this paper, we investigate how to recover causal relations at the original causal fre- quency from temporally aggregated data when k is known. Assuming the time series at the causal frequency follows a vector autoregressive (VAR) model, we show that the causal structure at the causal frequency is identifiable from aggregated time series if the noise terms are independent and non-Gaussian and some other technical conditions hold. We then present an estimation method based on non-Gaussian state-space modeling and eval- uate its performance on both synthetic and real data.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR15-gong17a, title = {Causal Discovery from Temporally Aggregated Time Series}, author = {Gong, Mingming and Zhang, Kun and Sch{\"o}lkopf, Bernhard and Glymour, Clark and Tao, Dacheng}, booktitle = {Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence}, pages = {161--170}, year = {2017}, editor = {Elidan, Gal and Kersting, Kristian}, volume = {R15}, series = {Proceedings of Machine Learning Research}, month = {11--15 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r15/main/assets/gong17a/gong17a.pdf}, url = {https://proceedings.mlr.press/r15/gong17a.html}, abstract = {Discovering causal structure of a dynamical sys- tem from observed time series is a traditional and important problem. In many practical ap- plications, observed data are obtained by apply- ing subsampling or temporally aggregation to the original causal processes, making it difficult to discover the underlying causal relations. Subsam- pling refers to the procedure that for every k con- secutive observations, one is kept, the rest being skipped, and recently some advances have been made in causal discovery from such data. With temporal aggregation, the local averages or sums of k consecutive, non-overlapping observations in the causal process are computed as new obser- vations, and causal discovery from such data is even harder. In this paper, we investigate how to recover causal relations at the original causal fre- quency from temporally aggregated data when k is known. Assuming the time series at the causal frequency follows a vector autoregressive (VAR) model, we show that the causal structure at the causal frequency is identifiable from aggregated time series if the noise terms are independent and non-Gaussian and some other technical conditions hold. We then present an estimation method based on non-Gaussian state-space modeling and eval- uate its performance on both synthetic and real data.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Causal Discovery from Temporally Aggregated Time Series %A Mingming Gong %A Kun Zhang %A Bernhard Schölkopf %A Clark Glymour %A Dacheng Tao %B Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2017 %E Gal Elidan %E Kristian Kersting %F pmlr-vR15-gong17a %I PMLR %P 161--170 %U https://proceedings.mlr.press/r15/gong17a.html %V R15 %X Discovering causal structure of a dynamical sys- tem from observed time series is a traditional and important problem. In many practical ap- plications, observed data are obtained by apply- ing subsampling or temporally aggregation to the original causal processes, making it difficult to discover the underlying causal relations. Subsam- pling refers to the procedure that for every k con- secutive observations, one is kept, the rest being skipped, and recently some advances have been made in causal discovery from such data. With temporal aggregation, the local averages or sums of k consecutive, non-overlapping observations in the causal process are computed as new obser- vations, and causal discovery from such data is even harder. In this paper, we investigate how to recover causal relations at the original causal fre- quency from temporally aggregated data when k is known. Assuming the time series at the causal frequency follows a vector autoregressive (VAR) model, we show that the causal structure at the causal frequency is identifiable from aggregated time series if the noise terms are independent and non-Gaussian and some other technical conditions hold. We then present an estimation method based on non-Gaussian state-space modeling and eval- uate its performance on both synthetic and real data. %Z Reissued by PMLR on 04 October 2026.
APA
Gong, M., Zhang, K., Schölkopf, B., Glymour, C. & Tao, D.. (2017). Causal Discovery from Temporally Aggregated Time Series. Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R15:161-170 Available from https://proceedings.mlr.press/r15/gong17a.html. Reissued by PMLR on 04 October 2026.

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