Counterfactual Evaluation of Treatment Assignment Functions with Networked Observational Data

Guo, Ruocheng; Li, Jundong; Liu, Huan

doi:10.1137/1.9781611976236.31

Cited by 21 publications

(15 citation statements)

References 23 publications

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“…We also have limited access to the background variables useful for controlling for confounding bias. For example, users' social network information -a common hidden confounder in observational studies -can be used to approximate their socio-economic status [21,65]. However, collecting personal data from social networking sites may contradict the terms of user privacy [66].…”

Section: Discussionmentioning

confidence: 99%

Evaluation Methods and Measures for Causal Learning Algorithms

Liu¹,

Guo²,

Moraffah³

et al. 2022

Preprint

Self Cite

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Section: Discussionmentioning

confidence: 99%

Evaluation Methods and Measures for Causal Learning Algorithms

Liu¹,

Guo²,

Moraffah³

et al. 2022

Preprint

Self Cite

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“…[54] applies GCN on network information to get the representation of hidden confounders. Moreover, in [53], graph attention layers are used to map the observed features in networked observational data to the Ddimensional space of partial latent confounders, by capturing the unknown edge weights in the real-world networked observational data.…”

Section: Relaxing Unconfoundedness Assumptionmentioning

confidence: 99%

A Survey on Causal Inference

Yao

Chu

et al. 2021

ACM Trans. Knowl. Discov. Data

261

131

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Causal inference is a critical research topic across many domains, such as statistics, computer science, education, public policy, and economics, for decades. Nowadays, estimating causal effect from observational data has become an appealing research direction owing to the large amount of available data and low budget requirement, compared with randomized controlled trials. Embraced with the rapidly developed machine learning area, various causal effect estimation methods for observational data have sprung up. In this survey, we provide a comprehensive review of causal inference methods under the potential outcome framework, one of the well-known causal inference frameworks. The methods are divided into two categories depending on whether they require all three assumptions of the potential outcome framework or not. For each category, both the traditional statistical methods and the recent machine learning enhanced methods are discussed and compared. The plausible applications of these methods are also presented, including the applications in advertising, recommendation, medicine, and so on. Moreover, the commonly used benchmark datasets as well as the open-source codes are also summarized, which facilitate researchers and practitioners to explore, evaluate and apply the causal inference methods.

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“…For example, Guo et al [110] proposed the network deconfounder to infer the influence of hidden confounders by mapping the features of observational data and auxiliary network information into the hidden space. Guo et al [111] leveraged the network information to recognize the representation of hidden confounders. Veitch et al [112] remarked that merely partial information that hidden confounders contain affects both the treatment and the outcome.…”

Section: Identifiabilitymentioning

confidence: 99%

A review of causality-based fairness machine learning

Su¹,

Gao²,

Wang³

et al. 2022

Intell Robot

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With the wide application of machine learning driven automated decisions (e.g., education, loan approval, and hiring) in daily life, it is critical to address the problem of discriminatory behavior toward certain individuals or groups. Early studies focused on defining the correlation/association-based notions, such as statistical parity, equalized odds, etc. However, recent studies reflect that it is necessary to use causality to address the problem of fairness. This review provides an exhaustive overview of notions and methods for detecting and eliminating algorithmic discrimination from a causality perspective. The review begins by introducing the common causality-based definitions and measures for fairness. We then review causality-based fairness-enhancing methods from the perspective of pre-processing, in-processing and post-processing mechanisms, and conduct a comprehensive analysis of the advantages, disadvantages, and applicability of these mechanisms. In addition, this review also examines other domains where researchers have observed unfair outcomes and the ways they have tried to address them. There are still many challenges that hinder the practical application of causality-based fairness notions, specifically the difficulty of acquiring causal graphs and identifiability of causal effects. One of the main purposes of this review is to spark more researchers to tackle these challenges in the near future.

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Counterfactual Evaluation of Treatment Assignment Functions with Networked Observational Data

Cited by 21 publications

References 23 publications

Evaluation Methods and Measures for Causal Learning Algorithms

Evaluation Methods and Measures for Causal Learning Algorithms

A Survey on Causal Inference

A review of causality-based fairness machine learning

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