Causal Discovery from Changes: a Bayesian Approach

By Jin Tian, Judea Pearl. 2001

38 citations recorded by the source corpus.

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Citations in the corpus

Causal Discovery from Heterogeneous/Nonstationary Data (2019, methodology): “The procedure in S 1 , which will be further discussed in Section 4.1, contains the methods proposed in (Hoover, 1990; Tian and Pearl, 2001; Peters et al., 2016) for causal discovery from changes as special cases.”
Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination (2017, methodology): “The procedure in Case 1 contains the methods proposed in [Hoover, 1990; Tian and Pearl, 2001] for causal discovery from changes as special cases, which may also be interpreted as special cases of the principle underlying the method for Case 2: if one of P (cause) and P (effect |cause) changes while ”
Multi-Source Domain Adaptation: A Causal View (2015, background): “Even if this is not the case, multiple source domains with different data distributions may allow one to identify the causal structure, since the causal knowledge can be seen from the change in data distributions; see e.g., (Tian and Pearl 2001).”
Discovery and Visualization of Nonstationary Causal Models (2015, methodology): “The procedure in Case 1 contains the methods proposed in [20, 21] for causal discovery from changes as special cases, which may also be interpreted as special cases of the principle underlying the method for Case 2: if one of P ( cause ) and P ( effect | cause ) changes while the other remains…”
Probabilistic Soft Interventions in Conditional Gaussian Networks (2005, methodology): “Tian and Pearl (2001) give an example for discrete networks, which can easily be extended to Gaussian networks by p(βγ , σ2γ | do(Yγ = k)) = d(β(0)γ − k) ∏ i∈pa(γ) d(β(i)γ ) · d(σ2γ).”
Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part I: Algorithms and Empirical Evaluation (2010, background): “Purnara and Wernisch (2004) proposed another active learning algorithm that uses a loss function defined in terms of the size of transition sequence equivalence class of networks (Tian and Pearl, 2001) and can handle continuous data.”
Domain Adaptation under Target and Conditional Shift (2013, background): “It has been demostrated that causal information can be derived from changes in data distributions (Tian & Pearl, 2001); on the other hand, knowledge of the data generating process, or causal knowledge, would imply how the data distribution changes across domains and help in domain adaptation.”
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).”