Related papers: Causal Discovery from Conditionally Stationary Time Series

Causal Discovery from Conditionally Stationary Time Series

URL: http://arxiv.org/abs/2110.06257v3
Date: Wed, 12 Feb 2025 16:47:01 GMT
Title: Causal Discovery from Conditionally Stationary Time Series
Authors: Carles Balsells-Rodas, Xavier Sumba, Tanmayee Narendra, Ruibo Tu, Gabriele Schweikert, Hedvig Kjellstrom, Yingzhen Li,
Abstract summary: We develop a causal discovery approach to handle a wide class of nonstationary time series. Named State-Dependent Causal Inference (SDCI), our approach is able to recover the underlying causal dependencies. Empirical experiments on nonlinear particle interaction data and gene regulatory networks demonstrate SDCI's superior performance.
Score: 14.297325665581353
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Abstract: Causal discovery, i.e., inferring underlying causal relationships from observational data, is highly challenging for AI systems. In a time series modeling context, traditional causal discovery methods mainly consider constrained scenarios with fully observed variables and/or data from stationary time-series. We develop a causal discovery approach to handle a wide class of nonstationary time series that are conditionally stationary, where the nonstationary behaviour is modeled as stationarity conditioned on a set of latent state variables. Named State-Dependent Causal Inference (SDCI), our approach is able to recover the underlying causal dependencies, with provable identifiablity for the state-dependent causal structures. Empirical experiments on nonlinear particle interaction data and gene regulatory networks demonstrate SDCI's superior performance over baseline causal discovery methods. Improved results over non-causal RNNs on modeling NBA player movements demonstrate the potential of our method and motivate the use of causality-driven methods for forecasting.

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