Generalized Score Functions for Causal Discovery

Huang, Biwei; Zhang, Kun; Lin, Yizhu; Schölkopf, Bernhard; Glymour, Clark

doi:10.1145/3219819.3220104

Cited by 66 publications

(48 citation statements)

References 30 publications

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“…In this paper we mainly focus on the continuous case. Readers who are interested in causal discovery from discrete variables or mixed discrete and continuous variables may refer to Peters et al (2010); Cai et al (2018); Huang et al (2018).…”

Section: Non-gaussian or Non-linear Methods Based On Functional Camentioning

confidence: 99%

Review of Causal Discovery Methods Based on Graphical Models

2019

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A fundamental task in various disciplines of science, including biology, is to find underlying causal relations and make use of them. Causal relations can be seen if interventions are properly applied; however, in many cases they are difficult or even impossible to conduct. It is then necessary to discover causal relations by analyzing statistical properties of purely observational data, which is known as causal discovery or causal structure search. This paper aims to give a introduction to and a brief review of the computational methods for causal discovery that were developed in the past three decades, including constraint-based and score-based methods and those based on functional causal models, supplemented by some illustrations and applications.

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Section: Non-gaussian or Non-linear Methods Based On Functional Camentioning

confidence: 99%

Review of Causal Discovery Methods Based on Graphical Models

2019

Self Cite

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“…The F 1 score has a maximum value of one and will achieve a good performance only when both Precision and Recall are high. It is a widely used measurement to estimate the performance of ML methods (Alioto et al., 2015; Huang et al., 2018; Luo et al., 2019; X. Zhang, Li, et al., 2019; T. Zhang, Lin, et al., 2019).…”

Section: Methodsmentioning

confidence: 99%

Improving Convection Trigger Functions in Deep Convective Parameterization Schemes Using Machine Learning

Zhang

Lin

Vogelmann

et al. 2021

J Adv Model Earth Syst

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Convection is critical to precipitation, heat and moisture transport, cloud amount and distribution, as well as to the global energy budget (Arakawa, 2004;Arakawa & Schubert, 1974). Properly representing convection is critical to successful numerical weather prediction (NWP) and climate simulations by general circulation models (GCMs). However, because current coarse-resolution models cannot explicitly resolve convection, it must be represented with convective parameterizations. Deficiencies in these parameterizations Abstract Deficiencies in convection trigger functions, used in deep convection parameterizations in General Circulation Models (GCMs), have critical impacts on climate simulations. A novel convection trigger function is developed using the machine learning (ML) classification model XGBoost. The large-scale environmental information associated with convective events is obtained from the longterm constrained variational analysis forcing data from the Atmospheric Radiation Measurement (ARM) program at its Southern Great Plains (SGP) and Manaus (MAO) sites representing, respectively, continental mid-latitude and tropical convection. The ML trigger is separately trained and evaluated per site, and jointly trained and evaluated at both sites as a unified trigger. The performance of the ML trigger is compared with four convective trigger functions commonly used in GCMs: dilute convective available potential energy (CAPE), undilute CAPE, dilute dynamic CAPE (dCAPE), and undilute dCAPE. The ML trigger substantially outperforms the four CAPE-based triggers in terms of the F1 score metric, widely used to estimate the performance of ML methods. The site-specific ML trigger functions can achieve, respectively, 91% and 93% F1 scores at SGP and MAO. The unified trigger also has a 91% F1 score, with virtually no degradation from the site-specific training, suggesting the potential of a global ML trigger function. The ML trigger alleviates a GCM deficiency regarding the overprediction of convection occurrence, offering a promising improvement to the simulation of the diurnal cycle of precipitation. Furthermore, to overcome the black box issue of the ML methods, insights derived from the ML model are discussed, which may be leveraged to improve traditional CAPE-based triggers.Plain Language Summary Deficiencies in convection trigger function, a set of conditions used to determine whether the convection will be activated at a given time in General Circulation Models (GCMs), have critical impacts on model simulated climate. This work presents a novel convection trigger function using a machine learning (ML) model. Environmental information on convective events are obtained from long-term data from the Atmospheric Radiation Measurement (ARM) program at its Southern Great Plains (SGP) and Manaus (MAO) sites, which represent two distinct convective regimes. The ML trigger is separately trained and evaluated per site, and jointly trained and evaluated at both sites as a unified trigger. The ML trigger substantially outperf...

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“…It is natural to investigate the causal structure that an ML model has inferred from the training data, establishing a link between causality and black box explanation. First, it is interesting to explore how the techniques for data-driven causal discovery, aimed at reconstructing plausible graphs of causal dependencies in observational data (see, e.g., (Huang et al 2018)), may be used to achieve more informative and robust explanations. Second, it is promising to explore how the techniques for causal inference may be used for driving the audit of a black box in the local explanation discovery.…”

Section: From Statistical To Causal Explanationsmentioning

confidence: 99%

Meaningful Explanations of Black Box AI Decision Systems

Pedreschi

Giannotti

Guidotti

et al. 2019

AAAI

131

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Black box AI systems for automated decision making, often based on machine learning over (big) data, map a user’s features into a class or a score without exposing the reasons why. This is problematic not only for lack of transparency, but also for possible biases inherited by the algorithms from human prejudices and collection artifacts hidden in the training data, which may lead to unfair or wrong decisions. We focus on the urgent open challenge of how to construct meaningful explanations of opaque AI/ML systems, introducing the local-toglobal framework for black box explanation, articulated along three lines: (i) the language for expressing explanations in terms of logic rules, with statistical and causal interpretation; (ii) the inference of local explanations for revealing the decision rationale for a specific case, by auditing the black box in the vicinity of the target instance; (iii), the bottom-up generalization of many local explanations into simple global ones, with algorithms that optimize for quality and comprehensibility. We argue that the local-first approach opens the door to a wide variety of alternative solutions along different dimensions: a variety of data sources (relational, text, images, etc.), a variety of learning problems (multi-label classification, regression, scoring, ranking), a variety of languages for expressing meaningful explanations, a variety of means to audit a black box.

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Generalized Score Functions for Causal Discovery

Cited by 66 publications

References 30 publications

Review of Causal Discovery Methods Based on Graphical Models

Review of Causal Discovery Methods Based on Graphical Models

Improving Convection Trigger Functions in Deep Convective Parameterization Schemes Using Machine Learning

Meaningful Explanations of Black Box AI Decision Systems

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