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Mathematics
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Computational Methods and Machine Learning for Causal Inference
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Computational Methods and Machine Learning for Causal Inference

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: 30 April 2024 | Viewed by 1572

Special Issue Editor

Prof. Dr. Bumba Mukherjee

E-Mail Website
Guest Editor
Department of Political Science, Pennsylvania State University, State College, PA 16802, USA
Interests: spatial statistics; statistical methodology; financial crisis

Special Issue Information

Dear Colleagues,

Assessing causality is challenging in the natural and social sciences. Yet, in recent years, causal inference has become vital for empirical evaluation across several fields such as computer science, economics, epidemiology, medical studies, political science, and sociology. Analyzing causal relationships is also critical for artificial intelligence (AI), as causality is necessary for overcoming limitations of predictions and assessment of correlations by machine learning. Evaluating causality in the context of AI is important as machine learning algorithms are widely used for decision making in key policymaking areas such as child welfare, criminal justice, public health, consumer lending, and medical trials.

In this Special Issue of Mathematics, we introduce readers to recent developments in causal inference across the natural and social sciences. To this end, the Special Issue pursues three goals. The first is to provide a comprehensive introduction to the computational implementation of different causal inference estimators from a historical perspective, where new estimators were developed to overcome the limitations of previous estimators. The second goal is to present original empirical research on computational causal inference and causal machine learning across a variety of fields. The third is to focus on advances in causal machine learning that address causal effect estimation for unstructured data, such as text and images.

Prof. Dr. Bumba Mukherjee
Guest Editor

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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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

  • computational causal inference
  • machine learning
  • unstructured data

Published Papers (2 papers)

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Research

17 pages, 861 KiB  
Article
Estimating the Individual Treatment Effect with Different Treatment Group Sizes
by Luyuan Song and Xiaojun Zhang
Mathematics 2024, 12(8), 1224; https://doi.org/10.3390/math12081224 - 18 Apr 2024
Viewed by 298
Abstract
Machine learning for causal inference, particularly at the individual level, has attracted intense interest in many domains. Existing techniques focus on controlling differences in distribution between treatment groups in a data-driven manner, eliminating the effects of confounding factors. However, few of the current [...] Read more.
Machine learning for causal inference, particularly at the individual level, has attracted intense interest in many domains. Existing techniques focus on controlling differences in distribution between treatment groups in a data-driven manner, eliminating the effects of confounding factors. However, few of the current methods adequately discuss the difference in treatment group sizes. Two approaches, a direct and an indirect one, deal with potential missing data for estimating individual treatment with binary treatments and different treatment group sizes. We embed the two methods into certain frameworks based on the domain adaption and representation. We validate the performance of our method by two benchmarks in the causal inference community: simulated data and real-world data. Experiment results verify that our methods perform well. Full article
(This article belongs to the Special Issue Computational Methods and Machine Learning for Causal Inference)
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34 pages, 7124 KiB  
Article
Exploratory Matching Model Search Algorithm (EMMSA) for Causal Analysis: Application to the Cardboard Industry
by Richard Aviles-Lopez, Juan de Dios Luna del Castillo and Miguel Ángel Montero-Alonso
Mathematics 2023, 11(21), 4506; https://doi.org/10.3390/math11214506 - 31 Oct 2023
Viewed by 882
Abstract
This paper aims to present a methodology for the application of matching methods in industry to measure causal effect size. Matching methods allow us to obtain treatment and control samples with their covariates as similar as possible. The matching techniques used are nearest, [...] Read more.
This paper aims to present a methodology for the application of matching methods in industry to measure causal effect size. Matching methods allow us to obtain treatment and control samples with their covariates as similar as possible. The matching techniques used are nearest, optimal, full, coarsened exact matching (CEM), and genetic. These methods have been widely used in medical, psychological, and economic sciences. The proposed methodology provides two algorithms to execute these methods and to conduct an exhaustive search for the best models. It uses three conditions to ensure, as far as possible, the balance of all covariates, the maximum number of units in the treatment and control groups, and the most significant causal effect sizes. These techniques are applied in the carton board industry, where the causal variable is downtime, and the outcome variable is waste generated. A dataset from the carton board industry is used, and the results are contrasted with an expert in this process. Meta-analysis techniques are used to integrate the results of different comparative studies, which could help to determine and prioritize where to reduce waste. Two machines were found to generate more waste in terms of standardized measures whose values are 0.52 and 0.53, representing 48.60 and 36.79 linear meters (LM) on average for each production order with a total downtime of more than 3000 s. In general, for all machines, the maximum average wastage for each production order is 24.98 LM and its confidence interval is [13.40;36.23] LM. The main contribution of this work is the use of causal methodology to estimate the effect of downtime on waste in an industry. Particularly relevant is the contribution of an algorithm that aims to obtain the best matching model for this application. Its advantages and disadvantages are evaluated, and future areas of research are outlined. We believe that this methodology can be applied to other industries and fields of knowledge. Full article
(This article belongs to the Special Issue Computational Methods and Machine Learning for Causal Inference)
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