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Causal inference in the medical domain: a survey
Causal inference is considered a crucial topic in the medical field, as it enables the determination of causal effects for medical treatments through...
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Disentangling causality: assumptions in causal discovery and inference
Causality has been a burgeoning field of research leading to the point where the literature abounds with different components addressing distinct...
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A Robust Bayesian Approach for Causal Inference Problems
Causal inference concerns finding the treatment effect on subjects along with causal links between the variables and the outcome. However, the...
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Causal Inference-Based Debiasing Framework for Knowledge Graph Completion
The task of Knowledge Graph Completion (KGC) entails inferring missing relations and facts in a partially specified graph to discover new knowledge....
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Can open access increase LIS research’s policy impact? Using regression analysis and causal inference
The relationship between open access and academic impact (usually measured as citations received from academic publications) has been extensively...
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Causal Inference in Data Analysis with Applications to Fairness and Explanations
Causal inference is a fundamental concept that goes beyond simple correlation and model-based prediction analysis, and is highly relevant in domains...
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Graph-Based Counterfactual Causal Inference Modeling for Neuroimaging Analysis
Alzheimer’s disease (AD) is a neurodegenerative disorder that is beginning with amyloidosis, followed by neuronal loss and deterioration in...
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Causal Inference and Non-randomized Experiments
Traditionally, machine learning and artificial intelligence focus on problems of diagnosis or prognosis. Answering questions on whether a patient...
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Deep treatment-adaptive network for causal inference
Causal inference is capable of estimating the treatment effect (i.e., the causal effect of treatment on the outcome ) to benefit the decision making...
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Application Potential for Causal Inference in Online Marketing
The application of causal diagrams together with the mathematical language of probability theory offers the opportunity to formulate questions of the...
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A Tool to Support Propensity Score Weighting for Enhanced Causal Inference in Business Processes
Effectively evaluating the impact of process interventions on business outcomes is crucial for assessing the effectiveness and return on investment...
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Towards Interpretable Defense Against Adversarial Attacks via Causal Inference
Deep learning-based models are vulnerable to adversarial attacks. Defense against adversarial attacks is essential for sensitive and safety-critical...
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Consistent causal inference from time series with PC algorithm and its time-aware extension
The estimator of a causal directed acyclic graph (DAG) with the PC algorithm is known to be consistent based on independent and identically...
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Research on Hierarchical Teaching Using Propensity Score Weighting-Based Causal Inference Model
The “double reduction” policy is an important initiative of the Party Central Committee to build a strong education country. In this context, how to...
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Determining the gender wage gap through causal inference and machine learning models: evidence from Chile
In the last decades, there has been increasing awareness of the different types of inequalities that women experience. A very important inequality is...
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Foundations of Causal ML
The present chapter covers the important dimension of causality in ML both in terms of causal structure discovery and causal inference. The vast...
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Causal inference for time series analysis: problems, methods and evaluation
Time series data are a collection of chronological observations which are generated by several domains such as medical and financial fields. Over the...
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FIGCI: Flow-Based Information-Geometric Causal Inference
This paper is concerned with causal discovery between two random variables X and Y with observational data. Information-Geometric Causal Inference...
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What Boosts Fake News Dissemination on Social Media? A Causal Inference View
There has been an upward trend of fake news propagation on social media. To solve the fake news propagation problem, it is crucial to understand...
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A Hybrid Medical Causal Inference Platform Based on Data Lake
Causal inference platform is rather useful in the medical domain, and relies heavily on data quality and prior knowledge. Nowadays, the advancement...