gCastle: A Python Toolbox for Causal Discovery

Zhang, Keli; Zhu, Shengyu; Kalander, Marcus; Ng, Ignavier; Ye, Junjian; Chen, Zhitang; Pan, Lujia

doi:10.48550/arxiv.2111.15155

Cited by 7 publications

(9 citation statements)

References 16 publications

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“…• Federated Causal Discovery [2,77]: Until this point, we suggested general causal discovery tools like gCastle [300] or benchpress [220]. However, the provided methods translate poorly into the federated setting due to the decentralized data.…”

Section: A Interpretabilitymentioning

confidence: 99%

See 1 more Smart Citation

A Review of the Role of Causality in Developing Trustworthy AI Systems

Ganguly¹,

Fazlija²,

Badar³

et al. 2023

Preprint

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State-of-the-art AI models largely lack an understanding of the cause-effect relationship that governs human understanding of the real world. Consequently, these models do not generalize to unseen data, often produce unfair results, and are difficult to interpret. This has led to efforts to improve the trustworthiness aspects of AI models. Recently, causal modeling and inference methods have emerged as powerful tools. This review aims to provide the reader with an overview of causal methods that have been developed to improve the trustworthiness of AI models. We hope that our contribution will motivate future research on causality-based solutions for trustworthy AI.

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Section: A Interpretabilitymentioning

confidence: 99%

“…• gCastle [300]: An end-to-end causal structure learning toolbox that is equipped with 19 techniques for Causal Discovery. It also assists users in data generation and evaluating learned structures.…”

Section: E2 Interesting Causal Toolsmentioning

confidence: 99%

A Review of the Role of Causality in Developing Trustworthy AI Systems

Ganguly¹,

Fazlija²,

Badar³

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…Causal discovery, a methodological paradigm dedicated to unearthing cause-and-effect relationships among variables, emerges as a formidable tool in scientific inquiry and data analysis. It enables researchers to transcend the boundaries of correlation, probing the underlying "why" behind observed phenomena (32)(33)(34). In the domain of NSSI, discerning the causal dynamics behind this complex behavior is crucial for crafting effective prevention and intervention measures.…”

Section: Introductionmentioning

confidence: 99%

Factors and pathways of non-suicidal self-injury in children: insights from computational causal analysis

Guo,

Wang,

et al. 2024

Front. Public Health

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BackgroundNon-suicidal self-injury (NSSI) has become a significant public health issue, especially prevalent among adolescents. The complexity and multifactorial nature of NSSI necessitate a comprehensive understanding of its underlying causal factors. This research leverages the causal discovery methodology to explore these causal associations in children.MethodsAn observational dataset was scrutinized using the causal discovery method, particularly employing the constraint-based approach. By integrating machine learning and causal inference techniques, the study aimed to determine direct causal relationships associated with NSSI. The robustness of the causal relationships was evaluated using three methods to construct and validate it: the PC (Peter and Clark) method, Fast Causal Inference (FCI) method, and the GAE (Graphical Autoencoder) method.ResultsAnalysis identified nine nodes with direct causal relationships to NSSI, including life satisfaction, depression, family dysfunction, sugary beverage consumption, PYD (positive youth development), internet addiction, COVID-19 related PTSD, academic anxiety, and sleep duration. Four principal causal pathways were identified, highlighting the roles of lockdown-induced lifestyle changes, screen time, positive adolescent development, and family dynamics in influencing NSSI risk.ConclusionsAn in-depth analysis of the factors leading to Non-Suicidal Self-Injury (NSSI), highlighting the intricate connections among individual, family, and pandemic-related influences. The results, derived from computational causal analysis, underscore the critical need for targeted interventions that tackle these diverse causative factors.

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“…These can result in spurious cause-effect relationships. To mitigate these challenges in practice, researchers augment causal learning with prior causal relations as featured in software packages such as CausalNex 1 , causal-learn 2 , bnlearn (Scutari, 2009), gCastle (Zhang et al, 2021), and DoWhy (Sharma and Kiciman, 2020). Heindorf et al (Heindorf et al, 2020) in their work attempts to construct the first large scale open domain causality graph that can be in-cluded in the existing knowledge bases.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Induced Expert Knowledge in Causal Structure Learning by NOTEARS

Chowdhury¹,

Rashid²,

Terejanu³

2023

Preprint

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Causal modeling provides us with powerful counterfactual reasoning and interventional mechanism to generate predictions and reason under various what-if scenarios. However, causal discovery using observation data remains a nontrivial task due to unobserved confounding factors, finite sampling, and changes in the data distribution. These can lead to spurious cause-effect relationships. To mitigate these challenges in practice, researchers augment causal learning with known causal relations. The goal of the paper is to study the impact of expert knowledge on causal relations in the form of additional constraints used in the formulation of the nonparametric NOTEARS. We provide a comprehensive set of comparative analyses of biasing the model using different types of knowledge. We found that (i) knowledge that correct the mistakes of the NOTEARS model can lead to statistically significant improvements, (ii) constraints on active edges have a larger positive impact on causal discovery than inactive edges, and surprisingly, (iii) the induced knowledge does not correct on average more incorrect active and/or inactive edges than expected. We also demonstrate the behavior of the model and the effectiveness of domain knowledge on a real-world dataset.

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gCastle: A Python Toolbox for Causal Discovery

Cited by 7 publications

References 16 publications

A Review of the Role of Causality in Developing Trustworthy AI Systems

A Review of the Role of Causality in Developing Trustworthy AI Systems

Factors and pathways of non-suicidal self-injury in children: insights from computational causal analysis

Evaluation of Induced Expert Knowledge in Causal Structure Learning by NOTEARS

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