Causal Inference, Probability Theory and Graphical Concepts

Dear Colleagues,

Finding causal relationships and their effects, not just statistical associations, has become one of the major subject areas in the disciplines of statistics, data and computer sciences, econometrics, epidemiology, bioinformatics, etc. It was David Hume (1748) who said that the only immediate utility of all the sciences is to teach us how to control and regulate future events through their causes. However, in legal contexts, etc., it is of interest to find the causes of effects rather than the effects of causes.  As such, most of the sciences and some other disciplines can be considered as being grounded in some kind of causality theory. Additionally, many of the estimation and assessment tasks that are involved in these disciplines should be performed using observational data instead of using the data generated by randomized experiments, mainly due to ethical or impractical or other similar reasons. This can be a harder task since we often find confounders and other types of biases such as selection bias in observation data that make the estimation of causal effects, etc., harder. Additionally, the essential components of the formulation of such estimation tasks as well as to find causality itself are causal assumptions, probability theory, certain graphical theories of representation of the causal dependence structure of the context, and counterfactual arguments. These theories have resulted in different causal effect estimation frameworks, such as the so-called probabilistic graphical models and the potential outcome model. 

This Special Issue focuses on causal models and their estimation and inference methods that are based on the probability theory, statistical regression theory, counterfactual arguments, and graphical and network concepts. Therefore, these models can also use information theory, optimization theory, machine learning methods, probabilistic and statistical predictive theory, Bayesian theory, etc.  Articles can be on the basic principles as well as on more advanced estimation and inference methods, algorithms, and applications in different disciples. Both original and review articles are welcome. Papers on the computational aspects, either tutorials or otherwise, are also welcome.

Topics of the papers include, but not limited to:

• Causal graphical models, do-calculus, and faithfulness;
• Potential outcome causal models;
• Confounding and balancing score estimation;
• Selection bias and collider bias;
• Causal parameter identification and estimation;
• Multivariate matching methods and sparse estimation;
• Robust causal inference and model misspecification;
• Causal discovery and constraint-based approaches;
• Machine learning for causal inference;
• Probability of causation;
• Granger causality and inferring causation in time series;
• Mediation analysis;
• Causal regression models;
• Causal inference in science, medicine, economics, and society;
• Big data and data-driven approaches;
• Predictive modeling and making causal claims;
• Outcome-dependent sampling and case-control studies;
• Categorical data analysis;
• Survival analysis;
• Sensitivity analysis for modeling assumptions.

Dr. Priyantha Wijayatunga
Dr. Linbo Wang
Dr. Wang Miao
Guest Editors

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