Causal Discovery from Heterogeneous/Nonstationary Data

By Biwei Huang, Kun Zhang, Jiji Zhang, J. Ramsey, Ruben Sanchez-Romero, Clark Glymour, Bernhard Schölkopf. 2019

316 citations recorded by the source corpus.

It is commonplace to encounter heterogeneous or nonstationary data, of which the underlying generating process changes across domains or over time. Such a distribution shift feature presents both challenges and opportunities for causal discovery. In this paper, we develop a framework for causal discovery from such data, called Constraint-based causal Discovery from heterogeneous/NOnstationary Data (CD-NOD), to find causal skeleton and directions and estimate the properties of mechanism changes. First, we propose an enhanced constraint-based procedure to detect variables whose local mechanisms change and recover the skeleton of the causal structure over observed variables. Second, we present a method to determine causal orientations by making use of independent changes in the data distribution implied by the underlying causal model, benefiting from information carried by changing distributions. After learning the causal structure, next, we investigate how to efficiently estimate the `driving force' of the nonstationarity of a causal mechanism. That is, we aim to extract from data a low-dimensional representation of changes. The proposed methods are nonparametric, with no hard restrictions on data distributions and causal mechanisms, and do not rely on window segmentation. Furthermore, we find that data heterogeneity benefits causal structure identification even with particular types of confounders. Finally, we show the connection between heterogeneity/nonstationarity and soft intervention in causal discovery. Experimental results on various synthetic and real-world data sets (task-fMRI and stock market data) are presented to demonstrate the efficacy of the proposed methods.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Causal Inference & Discovery

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Citations in the corpus

Toward Causal Representation Learning (2021, background): “The SMS hypothesis was stated in [25], [114], [180], and [217] and in earlier form in [219], [220], and [281].”
Towards Causal Representation Learning (2021, background): “263, 243, 195, 152, 156, 153, 167, 204, 115], complementing the studies of Bareinboim and Pearl [12, 185], and it inspired a thread of work in the statistics community exploiting invariance for causal discovery and other tasks [189, 192, 105, 104, 115].”
D’ya Like DAGs? A Survey on Structure Learning and Causal Discovery (2021, unknown): “PC [223] 1993 constraint yes yes yes no CPDAG CCD [196] 1996 constraint yes yes both no PAG FCI [223] 2000 constraint no yes yes no PAG TPDA [31] 2002 constraint yes yes yes no CPDAG CPC [190] 2006 constraint yes relaxed yes no CPDAG KCL [227] 2007 constraint yes yes yes no CPDAG ION [234] 2008 cons”
Specific and Shared Causal Relation Modeling and Mechanism-Based Clustering (2019, background): “For example, causal discovery from heterogeneous/nonstationary data (CD-NOD) [38, 19] concatenates data from different domains and considers the domain index as a surrogate to characterize the variability of causal mechanisms, and ﬁnally recovers ﬁxed as well as varying causal relations.”
AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning (2021, background): “It has been shown that under the Markov condition and faithfulness assumption, for every Vi, Vj ∈ V, Vi and Vj are not adjacent in G if and only if they are independent conditional on some subset of {Vl|l 6= i, l 6= j} ∪ k (Huang et al., 2020).”