Behind Distribution Shift: Mining Driving Forces of Changes and Causal Arrows

By Biwei Huang, Kun Zhang, Jiji Zhang, Ruben Sanchez-Romero, Clark Glymour, Bernhard Schölkopf. 2017

38 citations recorded by the source corpus.

We address two important issues in causal discovery from nonstationary or heterogeneous data, where parameters associated with a causal structure may change over time or across data sets. First, we investigate how to efficiently estimate the "driving force" of the nonstationarity of a causal mechanism. That is, given a causal mechanism that varies over time or across data sets and whose qualitative structure is known, we aim to extract from data a low-dimensional and interpretable representation of the main components of the changes. For this purpose we develop a novel kernel embedding of nonstationary conditional distributions that does not rely on sliding windows. Second, the embedding also leads to a measure of dependence between the changes of causal modules that can be used to determine the directions of many causal arrows. We demonstrate the power of our methods with experiments on both synthetic and real data.

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

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Citations in the corpus

Weakly-Supervised Disentanglement Without Compromises (2020, background): “A parallel thread of work has studied distribution shifts by identifying changes in causal generative factors (Zhang et al., 2015; 2017; Huang et al., 2017), which is linked to a causal view of disentanglement (Suter et al., 2019; Schölkopf, 2019).”
Causality for Machine Learning (2019, background): “The line of work provided rather fruitful (Zhang et al., 2013; Weichwald et al., 2014; Zhang et al., 2015; Gong et al., 2016; Huang et al., 2017; Blöbaum et al., 2016; Guo et al., 2018; Wang et al., 2019; Subbaswamy et al., 2018; Rabanser et al., 2018; Li et al., 2018a; Lipton et al., 2018; Li et al”
Causal Discovery from Heterogeneous/Nonstationary Data (2019, methodology): “We compared with the window-based method (Zhang et al., 2017), the IB method, and the MC method for direction determination.”
Generalized Score Functions for Causal Discovery (2018, background): “As a consequence, they can be easily extended to handle more complex situations, such as the case with nonstationary time series or multiple, heterogeneous data sets [16, 32].”
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 [25], and some other methods can directly estimate varying causal relations [18, 20, 11, 36, 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).”
Review of Causal Discovery Methods Based on Graphical Models (2019, unknown): “This, in particular, inspires a framework for causal mechanism change detection, causal skeleton estimation, causal direction identiﬁcation, and nonstationary driving force estimation (Huang et al., 2017; Zhang et al., 2017b).”