eCDANs: Efficient Temporal Causal Discovery from Autocorrelated and Non-stationary Data (Student Abstract)
Conventional temporal causal discovery (CD) methods suffer from high dimensionality, fail to identify lagged causal relationships, and often ignore dynamics in relations. In this study, we present a novel constraint-based CD approach for autocorrelated and non-stationary time series data (eCDANs) capable of detecting lagged and contemporaneous causal relationships along with temporal changes. eCDANs addresses high dimensionality by optimizing the conditioning sets while conducting conditional independence (CI) tests and identifies the changes in causal relations by introducing a surrogate variable to represent time dependency. Experiments on synthetic and real-world data show that eCDANs can identify time influence and outperform the baselines.
Code (0)
등록된 구현이 없습니다.
Tasks
Causal DiscoveryTime SeriesTime Series AnalysisMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
CDANs: Temporal Causal Discovery from Autocorrelated and Non-Stationary Time Series Data
Time series data are found in many areas of healthcare such as medical time series, electronic health records (EHR), measurements of vitals, and wearable devices. Causal discovery, which involves estimating causal relati…
Causal DiscoveryTime SeriesTime Series AnalysisDCD: Decomposition-based Causal Discovery from Autocorrelated and Non-Stationary Temporal Data
Multivariate time series in domains such as finance, climate science, and healthcare often exhibit long-term trends, seasonal patterns, and short-term fluctuations, complicating causal inference under non-stationarity an…
Causal InferenceSortability of Time Series Data
Evaluating the performance of causal discovery algorithms that aim to find causal relationships between time-dependent processes remains a challenging topic. In this paper, we show that certain characteristics of dataset…
Causal DiscoveryTime SeriesHigh-recall causal discovery for autocorrelated time series with latent confounders
We present a new method for linear and nonlinear, lagged and contemporaneous constraint-based causal discovery from observational time series in the presence of latent confounders. We show that existing causal discovery …
Causal DiscoveryTime SeriesTime Series AnalysisVocal Bursts Intensity PredictionTTCD:Transformer Integrated Temporal Causal Discovery from Non-Stationary Time Series Data
The widespread availability of complex time series data in various domains such as environmental science, epidemiology, and economics demands robust causal discovery methods that can identify intricate contemporaneous an…
Change Point Detection