Search for Additive Nonlinear Time Series Causal Models

By Tianjiao Chu, Clark Glymour. 2008

115 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination (2017, background): “However, we note that it is natural to generalize our framework to incorporate time-delayed causal relations in time series, just in the way that constraint-based causal discovery was adapted to handle time-series data (see, e.g., [Chu and Glymour, 2008]).”
Invariant Causal Prediction for Sequential Data (2017, methodology): “Other methods that are able to identify instantaneous effects (e.g., Chu and Glymour 2008; Hyvärinen, Shimizu, and Hoyer 2008; Peters, Janzing, and Schölkopf 2013) often require nonlinearities or non-Gaussianity.”
Masked Gradient-Based Causal Structure Learning (2019, methodology): “For time series data, several methods [27, 24, 6] ﬁrst choose a time lag p ≥ 0 and then perform causal discovery among d ( p + 1) variables, where many edges can be excluded as the variable at the current time cannot be a cause of the one of past time.”
Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components (2014, background): “4We are aware that nonlinear models (Chu & Glymour, 2008) and nonparametric estimators (Schreiber, 2000) have been used to find temporal causal relations.”
Discovery and Visualization of Nonstationary Causal Models (2015, background): “However, we note that it is natural to generalize our framework to incorporate time-delayed causal relations, just in the way that constraint-based causal discovery was adapted to handle time-series data (see, e.g., [34]).”
A Survey of Learning Causality with Data: Problems and Methods (2018, methodology): “Researchers [29, 39] proposed to adapt FCI algorithm for time series.”
Causality : Objectives and Assessment (2010, background): “, 2000; Kononenko, 2009), epidemiology (Aickin, 2002), climatology (Chu and Glymour, 2008), social and economic sciences (Kaplan, 2000; Demiralp and Hoover, 2003; Moneta, 2005), marketing (CFMDCY, 2006), neuroscience (Ding et al.”
Causal Discovery with Attention-Based Convolutional Neural Networks (2019, background): “Additive Non-linear Time Series Model (ANLTSM) [20] does causal discovery in both linear and non-linear time series data, and can also deal with hidden confounders.”
Causal Inference on Time Series using Structural Equation Models (2012, methodology): “Following Bell et al. [1996], Chu and Glymour [2008] introduce additive nonlinear time series models (ANLTSM for short) for performing relaxed conditional independence tests: If including one variable, e.g.”
Causal Inference on Time Series using Restricted Structural Equation Models (2013, methodology): “|X t−p , . . . , X t−1 ] for additive noise) we require stationarity and/or α mixing, or geometric ergodicity [e.g. Chu and Glymour, 2008].”