Publications

Stationarity-Aware Causal Discovery in Time Series via Minimal Separating Sets

Shanyun Gao, Raghavendra Addanki, Tong Yu, Ryan A. Rossi, Qifan Song, Murat Kocaoglu

The 29th International Conference on Artificial Intelligence and Statistics (AISTATS), 2026

Abstract. Discovering causal relationships from observational time series is a fundamental problem with broad applications in climate science, healthcare, and finance. Causal graphs with time-lagged structure capture the effects of underlying mechanisms over time. Under the causal stationarity assumption, these causal mechanisms remain consistent across time. Existing constraint-based methods leverage stationarity for conditional independence testing and reduce the problem to learning the parents of variables at the final time point, which can then be used to reconstruct the stationary graph. However, their separating set search strategy mimics the PC algorithm and does not take advantage of the stationary structure. We observe that the stationary graph structure and autoregressive edges impose many meaningful constraints on the separating sets between variables at different time lags. After characterizing the behavior of such separating sets, we propose a novel causal discovery algorithm that exploits this structure of minimal separating sets. Extensive evaluations on synthetic and real-world datasets demonstrate the robustness and accuracy of our method.

@inproceedings{gao2026stationarity,
  title     = {Stationarity-Aware Causal Discovery in Time Series via Minimal Separating Sets},
  author    = {Gao, Shanyun and Addanki, Raghavendra and Yu, Tong and Rossi, Ryan A. and Song, Qifan and Kocaoglu, Murat},
  booktitle = {Proceedings of the 29th International Conference on Artificial Intelligence and Statistics},
  series    = {Proceedings of Machine Learning Research},
  volume    = {300},
  year      = {2026}
}

Causal Discovery-Driven Change Point Detection in Time Series

Shanyun Gao, Raghavendra Addanki, Tong Yu, Ryan A. Rossi, Murat Kocaoglu

The 28th International Conference on Artificial Intelligence and Statistics (AISTATS), 2025

Abstract. Change point detection in time series aims to identify moments when the probability distribution of time series changes. It is widely applied in many areas, such as human activity sensing and medical science. In the context of multivariate time series, this typically involves examining the joint distribution of multiple variables: If the distribution of any one variable changes, the entire time series undergoes a distribution shift. However, in practical applications, we may be interested only in certain components of the time series, exploring abrupt changes in their distributions while accounting for the presence of other components. Here, assuming an underlying structural causal model that governs the time-series data generation, we address this task by proposing a two-stage non-parametric algorithm that first learns parts of the causal structure through constraint-based discovery methods, and then employs conditional relative Pearson divergence estimation to identify the change points. The conditional relative Pearson divergence quantifies the distribution difference between consecutive segments in the time series, while the causal discovery method allows a focus on the causal mechanism, facilitating access to independent and identically distributed (IID) samples. Theoretically, the typical assumption of samples being IID in conventional change point detection methods can be relaxed based on the Causal Markov Condition. Through experiments on both synthetic and real-world datasets, we validate the correctness and utility of our approach.

@inproceedings{gao2025causal,
  title     = {Causal Discovery-Driven Change Point Detection in Time Series},
  author    = {Gao, Shanyun and Addanki, Raghavendra and Yu, Tong and Rossi, Ryan A. and Kocaoglu, Murat},
  booktitle = {Proceedings of the 28th International Conference on Artificial Intelligence and Statistics},
  series    = {Proceedings of Machine Learning Research},
  volume    = {258},
  year      = {2025}
}

Causal Discovery in Semi-Stationary Time Series

Shanyun Gao, Raghavendra Addanki, Tong Yu, Ryan A. Rossi, Murat Kocaoglu

Advances in Neural Information Processing Systems (NeurIPS), 36: 46624–46657, 2023

Abstract. Discovering causal relations from observational time series without making the stationary assumption is a significant challenge. In practice, this challenge is common in many areas, such as retail sales, transportation systems, and medical science. Here, we consider this problem for a class of non-stationary time series. The structural causal model (SCM) of this type of time series, called the semi-stationary time series, exhibits that a finite number of different causal mechanisms occur sequentially and periodically across time. This model holds considerable practical utility because it can represent periodicity, including common occurrences such as seasonality and diurnal variation. We propose a constraint-based, non-parametric algorithm for discovering causal relations in this setting. The resulting algorithm, PCMCI_Ω, can capture the alternating and recurring changes in the causal mechanisms and then identify the underlying causal graph with conditional independence (CI) tests. We show that this algorithm is sound in identifying causal relations on discrete-valued time series. We validate the algorithm with extensive experiments on continuous and discrete simulated data. We also apply our algorithm to a real-world climate dataset.

@inproceedings{gao2023causal,
  title     = {Causal Discovery in Semi-Stationary Time Series},
  author    = {Gao, Shanyun and Addanki, Raghavendra and Yu, Tong and Rossi, Ryan A. and Kocaoglu, Murat},
  booktitle = {Advances in Neural Information Processing Systems},
  volume    = {36},
  pages     = {46624--46657},
  year      = {2023}
}

Causal In-Context Learning in Transformers: Training Dynamics Across Heterogeneous Interventional Data

Shanyun Gao, Murat Kocaoglu, Qifan Song

Workshop on Causality in the Age of AI Scaling, AISTATS, 2026

Causal Discovery in Semi-Stationary Time Series

Shanyun Gao, Raghavendra Addanki, Tong Yu, Ryan A. Rossi, Murat Kocaoglu

Causal Inference for Time Series Data Workshop, UAI, 2023  Oral presentation

Transformers Implicitly Perform Local Causal Discovery from Diverse Data

Shanyun Gao, Murat Kocaoglu, Qifan Song

Manuscript in preparation