Multi-domain Causal Structure Learning in Linear Systems

By AmirEmad Ghassami, Negar Kiyavash, Biwei Huang, Kun Zhang. 2018

65 citations recorded by the source corpus.

We study the problem of causal structure learning in linear systems from observational data given in multiple domains, across which the causal coefficients and/or the distribution of the exogenous noises may vary. The main tool used in our approach is the principle that in a causally sufficient system, the causal modules, as well as their included parameters, change independently across domains. We first introduce our approach for finding causal direction in a system comprising two variables and propose efficient methods for identifying causal direction. Then we generalize our methods to causal structure learning in networks of variables. Most of previous work in structure learning from multi-domain data assume that certain types of invariance are held in causal modules across domains. Our approach unifies the idea in those works and generalizes to the case that there is no such invariance across the domains. Our proposed methods are generally capable of identifying causal direction from fewer than ten domains. When the invariance property holds, two domains are generally sufficient.

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Citations in the corpus

Causal Discovery from Heterogeneous/Nonstationary Data (2019, methodology): “Recently, several methods have been proposed to tackle time-varying or domain-varying instantaneous causal relations (Ghassami et al., 2018; Huang et al., 2019a,b, 2020).”
Specific and Shared Causal Relation Modeling and Mechanism-Based Clustering (2019, methodology): “We compared it with other well-known approaches in causal discovery, including LiNGAM [32], the minimal change method (MC) [11], and the identical boundaries method (IB) [11].”
AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning (2021, background): “It is commonplace that given a very high-dimensional input, only a few factors may change, which is known as minimal change principle [Ghassami et al., 2018] or sparse mechanism shift assumption [Schölkopf et al.”
Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models (2019, methodology): “, 2018), and the identical boundaries method (IB) (Ghassami et al., 2018).”