A Survey of Learning Causality with Data: Problems and Methods

Guo, Ruocheng; Cheng, Lu; Li, Jundong; Hahn, P. Richard; Liu, Huan

doi:10.48550/arxiv.1809.09337

Cited by 13 publications

(14 citation statements)

References 59 publications

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“…We were intrigued with these results since we felt they provided some structural insight into physical properties of learning problems, thus going beyond the applications or methodological advances that machine learning studies usually provided. 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;Subbaswamy et al, 2018;Rabanser et al, 2018;Li et al, 2018a;Li et al, 2018b;Magliacane et al, 2018;, and nicely complementary to studies of Elias Bareinboim and Judea (Bareinboim and Pearl, 2014;.…”

Section: Half-sibling Regression and Exoplanet Detectionmentioning

confidence: 95%

Causality for Machine Learning

Schölkopf¹

2019

Preprint

123

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Graphical causal inference as pioneered by Judea Pearl arose from research on artificial intelligence (AI), and for a long time had little connection to the field of machine learning. This article discusses where links have been and should be established, introducing key concepts along the way. It argues that the hard open problems of machine learning and AI are intrinsically related to causality, and explains how the field is beginning to understand them.

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Section: Half-sibling Regression and Exoplanet Detectionmentioning

confidence: 95%

Causality for Machine Learning

Schölkopf¹

2019

Preprint

123

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“…While Granger Causality provides a powerful tool for understanding which neural time series have a key role in predicting the future of other neural time series [84][85][86], studies express concern since prediction is not a formal setting to answer causal questions related to the consequence of interventions and counterfactuals [87][88][89]. Furthermore, in practice, GC uses a model assumption between the variables, e.g.…”

Section: Granger Causalitymentioning

confidence: 99%

Statistical Perspective on Functional and Causal Neural Connectomics: A Comparative Study

Biswas,

Shlizerman

2021

Preprint

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Representation of brain network interactions is fundamental to the translation of neural structure to brain function. As such, methodologies for mapping neural interactions into structural models, i.e., inference of functional connectome from neural recordings, are key for the study of brain networks. While multiple approaches have been proposed for functional connectomics based on statistical associations between neural activity, association does not necessarily incorporate causation. Additional approaches have been proposed to incorporate aspects of causality to turn functional connectomes into causal functional connectomes, however, these methodologies typically focus on specific aspects of causality. This warrants a systematic statistical framework for causal functional connectomics that defines the foundations of common aspects of causality. Such a framework can assist in contrasting existing approaches and to guide development of further causal methodologies. In this work, we develop such a statistical guide. In particular, we consolidate the notions of associations and representations of neural interaction, i.e., types of neural connectomics, and then describe causal modeling in the statistics literature. We particularly focus on the introduction of directed Markov graphical models as a framework through which we define the Directed Markov Property -an essential criterion for examining the causality of proposed functional connectomes. We demonstrate how based on these notions, a comparative study of several existing approaches for finding causal functional connectivity from neural activity can be conducted. We proceed by providing an outlook ahead regarding the additional properties that future approaches could include to thoroughly address causality.Preprint. Under review.

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“…For the interested reader, an in-depth discussion on the social, philosophical, cognitive, and psychological aspects of explanations can be found at [21,35,125,174]. For XAI, see [1,2,31,55,61,67,75,111,120,128], for causal and counterfactual inference, see [63,76,131,145,146,159,185], and for optimization, see [32,137,166].…”

Section: Motivationmentioning

confidence: 99%

A survey of algorithmic recourse: definitions, formulations, solutions, and prospects

Karimi¹,

Barthe²,

Schölkopf³

et al. 2020

Preprint

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Machine learning is increasingly used to inform decision-making in sensitive situations where decisions have consequential effects on individuals' lives. In these settings, in addition to requiring models to be accurate and robust, socially relevant values such as fairness, privacy, accountability, and explainability play an important role for the adoption and impact of said technologies. In this work, we focus on algorithmic recourse, which is concerned with providing explanations and recommendations to individuals who are unfavourably treated by automated decision-making systems. We first perform an extensive literature review, and align the efforts of many authors by presenting unified definitions, formulations, and solutions to recourse. Then, we provide an overview of the prospective research directions towards which the community may engage, challenging existing assumptions and making explicit connections to other ethical challenges such as security, privacy, and fairness.

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A Survey of Learning Causality with Data: Problems and Methods

Cited by 13 publications

References 59 publications

Causality for Machine Learning

Causality for Machine Learning

Statistical Perspective on Functional and Causal Neural Connectomics: A Comparative Study

A survey of algorithmic recourse: definitions, formulations, solutions, and prospects

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