Identification of Time-Dependent Causal Model: A Gaussian Process Treatment

By Biwei Huang, Kun Zhang, Bernhard Schölkopf. 2015

47 citations recorded by the source corpus.

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Citations in the corpus

Causal Discovery from Heterogeneous/Nonstationary Data (2019, methodology): “Some methods aim to estimate the time-varying causal model by making use of certain types of smoothness of the change (Huang et al., 2015), but they do not explicitly locate the nonstationary causal modules.”
Behind Distribution Shift: Mining Driving Forces of Changes and Causal Arrows (2017, positive): “We compared our approach with linear timedependent functional causal model [4], which puts a GP prior on time-varying coefficients and uses the mean of the posterior to represent the nonstationary driving force.”
Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination (2017, methodology): “Some methods aim to estimate the time-varying causal model by making use of certain types of smoothness of the change [ Huang et al. , 2015 ] , but they do not explicitly locate the changing causal modules.”
DYNOTEARS: Structure Learning from Time-Series Data (2020, background): “Another topic for future work is to investigate the behaviour of the algorithm on nonstationary or cointegrated time series (Malinsky and Spirtes, 2019), or in situations with confounders (Huang et al., 2015; Malinsky and Spirtes, 2018).”
Discovery and Visualization of Nonstationary Causal Models (2015, methodology): “Some methods aim to estimate the time-varying causal model by making use of certain types of smoothness of the change [15], but they do not explicitly locate the nonstationary causal modules.”
A Bayesian Nonparametic Approach for Estimating Individualized Treatment-Response Curves (2016, background): “Only recently, Huang et al. (2015) and Xu and Ji (2014) have used parametric models to estimate treatment effects over time.”
Specific and Shared Causal Relation Modeling and Mechanism-Based Clustering (2019, background): “In the linear case, invariant causal relations are found based on invariant predictions [26], and some other methods can directly estimate varying causal relations [18, 20, 11, 37, 15].”
Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models (2019, background): “In many scientiﬁc ﬁelds, we are often faced with (Hoover, 1990; Tian & Pearl, 2001; Huang et al., 2015; Zhang et al., 2017; Huang et al., 2017; Peters et al., 2016; Ghassami et al., 2018).”