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Causal Discovery from Conditionally Stationary Time Series
Machine Learning · Submitted 12 Oct 2021
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Empirical experiments on nonlinear particle interaction data and gene regulatory networks demonstrate SDCI's superior performance over baseline causal discovery methods.
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The abstract, sentence by sentence
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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Causal discovery, i.e., inferring underlying causal relationships from observational data, is highly challenging for AI systems. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s1 | 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. | background 0.70 | 0.04 | 0.02 | asserted 1.00 |
| s2 | 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. | contribution 0.77 | 0.93 | 0.19 | asserted 1.00 |
| s3 | 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. | contribution 0.78 | 0.93 | 0.35 | asserted 1.00 |
| s4 | Empirical experiments on nonlinear particle interaction data and gene regulatory networks demonstrate SDCI's superior performance over baseline causal discovery methods. | result 1.00 | 0.95 | 0.34 | asserted 1.00 |
| s5 | 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. | interpretation 0.71 | 0.91 | 0.13 | asserted 0.92 |
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The paper
Original 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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References
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2 arXiv papers cite this in 4 sentences
Papers citing it · 1 sentence without a confident label
Cites it as background 1 paper
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Causal discovery from non-stationary temporal data remains an active research area [ref] and several advanced methods have been proposed in constraint-based [ref] and score-based [ref] categories for this task.
TTCD:Transformer Integrated Temporal Causal Discovery from Non-Stationary Time Series Data · Introduction · 0.94
What it cites
Cites as background 1 paper
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Other practical issues consist on the existence of latent confounders [ref] , the presence of measurement error [ref] or considering observations with missing data [ref] .
Multiple Causal Inference with Latent Confounding · Related work · 0.88
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