Learning Causal Structures Using Regression Invariance

By AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Kun Zhang. 2017

74 citations recorded by the source corpus.

We study causal inference in a multi-environment setting, in which the functional relations for producing the variables from their direct causes remain the same across environments, while the distribution of exogenous noises may vary. We introduce the idea of using the invariance of the functional relations of the variables to their causes across a set of environments. We define a notion of completeness for a causal inference algorithm in this setting and prove the existence of such algorithm by proposing the baseline algorithm. Additionally, we present an alternate algorithm that has significantly improved computational and sample complexity compared to the baseline algorithm. The experiment results show that the proposed algorithm outperforms the other existing algorithms.

Subfields: Artificial Intelligence, Control and Systems Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Yoshua Bengio
2. Mohit Bansal
3. Quoc V. Le
4. Jianfeng Gao
5. Lawrence Carin
6. Sergey Levine
7. Percy Liang
8. Kyunghyun Cho

In Control and Systems Engineering

1. Yuhao Wang
2. Chandler Squires
3. David Lopez-Paz
4. Eduard H. Hovy
5. Lawrence Carin
6. Ricardo Henao
7. Pradeep Ravikumar
8. Stefan Bauer

Citations in the corpus

• Learning Neural Causal Models from Unknown Interventions (2019, background): “Recently Peters et al. (2016); Ghassami et al. (2017) proposed to exploit invariance across different environments to infer causal structure, but are difficult to scale to large graphs due to the necessary iteration over the super-exponential set of possible graphs.”
• Invariant Risk Minimization (2019, background): “Some works in machine learning [45, 18, 21, 26, 36, 43, 34, 7] pursue similar questions.”
• Explaining The Efficacy of Counterfactually-Augmented Data (2020, background): “…where the relationship between a target variable and its direct causal ancestors remains invariant across foreseeable shifts (Peters et al., 2016; Ghassami et al., 2017; Rojas-Carulla et al., 2018; Kuang et al., 2018; Magliacane et al., 2018; Christiansen & Peters, 2020; Weichwald & Peters,…”
• The Risks of Invariant Risk Minimization (2020, background): “Various works on invariant prediction (Muandet et al., 2013; Ghassami et al., 2017; Heinze-Deml et al., 2018; Rojas-Carulla et al., 2018; Subbaswamy et al., 2019; Christiansen et al., 2020) consider regression in both the linear and non-linear setting, but they exclusively focus on learning with…”
• Permutation-Based Causal Structure Learning with Unknown Intervention Targets (2019, methodology): “A different approach to this problem is given by the invariant causal inference framework (Meinshausen et al., 2016; Rothenhäusler et al., 2015; Ghassami et al., 2017).”
• Proactive Pseudo-Intervention: Causally Informed Contrastive Learning For Interpretable Vision Models (2020, background): “In the causality literature, z is commonly referred to as the treat-ment [2, 20].”