Causal Inference, Probability Theory and Graphical Concepts

A special issue of Computation (ISSN 2079-3197). This special issue belongs to the section "Computational Engineering".

Deadline for manuscript submissions: 31 July 2024 | Viewed by 4458

Special Issue Editors


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Guest Editor
Department of Statistics, Umeå School of Business, Economics and Statistics, Umeå University, 90781 Umeå, Sweden
Interests: causal inference; probabilistic modeling and reasoning; artificial intelligence; machine learning; medical statistics

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Guest Editor
Department of Statistical Sciences, University of Toronto, Toronto, Canada
Interests: causality
Department of Probability and Statistics, School of Mathematical Sciences, Peking University, Beijing 100871, China
Interests: causal inference; missing data; semi-parametric models; graphical models; epidemiological methods; statistical inference

Special Issue Information

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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Computation is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • causal assumptions
  • counterfactuals
  • statistical dependence
  • conditional (in)dependence structure
  • graphical representation
  • causal parameters
  • (semi)-parametric and non-parametric models
  • statistical estimation
  • predictive inference
  • latent variables
  • instrumental variables
  • confounding
  • collider and selection bias
  • covariate balance
  • algorithms
  • optimization
  • subject domain knowledge
  • information

Published Papers (2 papers)

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Research

17 pages, 2681 KiB  
Article
Quantifying Causal Path-Specific Importance in Structural Causal Model
by Xiaoxiao Wang, Minda Zhao, Fanyu Meng, Xin Liu, Zhaodan Kong and Xin Chen
Computation 2023, 11(7), 133; https://doi.org/10.3390/computation11070133 - 7 Jul 2023
Viewed by 1383
Abstract
Path-specific effect analysis is a powerful tool in causal inference. This paper provides a definition of causal counterfactual path-specific importance score for the structural causal model (SCM). Different from existing path-specific effect definitions, which focus on the population level, the score defined in [...] Read more.
Path-specific effect analysis is a powerful tool in causal inference. This paper provides a definition of causal counterfactual path-specific importance score for the structural causal model (SCM). Different from existing path-specific effect definitions, which focus on the population level, the score defined in this paper can quantify the impact of a decision variable on an outcome variable along a specific pathway at the individual level. Moreover, the score has many desirable properties, including following the chain rule and being consistent. Finally, this paper presents an algorithm that can leverage these properties and find the k-most important paths with the highest importance scores in a causal graph effectively. Full article
(This article belongs to the Special Issue Causal Inference, Probability Theory and Graphical Concepts)
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10 pages, 252 KiB  
Communication
Spillover Effects in Empirical Corporate Finance: Choosing the Proxy for Treatment Coverage
by Fabiana Gómez and David Pacini
Computation 2022, 10(9), 149; https://doi.org/10.3390/computation10090149 - 31 Aug 2022
Viewed by 1669
Abstract
The existing literature indicates that spillovers can lead to a complicated bias in the estimation of causal effects in empirical corporate finance. We show that, under the assumption of simple random treatment assignment and when the proxy chosen for the group-level treatment coverage [...] Read more.
The existing literature indicates that spillovers can lead to a complicated bias in the estimation of causal effects in empirical corporate finance. We show that, under the assumption of simple random treatment assignment and when the proxy chosen for the group-level treatment coverage is the leave-one-out average treatment, such a spillover bias exists if and only if the average indirect effects on the treated and untreated groups are different. We quantify the gains in spillover bias reduction using Monte Carlo exercises. We propose a Wald test to statistically infer the presence of bias. We illustrate the application of this test to bear out spillovers in firms’ employment decisions. Full article
(This article belongs to the Special Issue Causal Inference, Probability Theory and Graphical Concepts)
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