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An Overview of Recent Advancements in Causal Studies

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Abstract

In causal study we are interested in finding the graphical structure in the form of directed acyclic graphs (DAGs). These DAGs describe the directions and connection strength to connecting variables represented by nodes. In this regard, various methods have been developed to estimate the appropriate structure of the causal model and to explain a fair number of its features. Our review aims to provide a complete and systematic analysis of selected articles from past few decades, having powerful methods to infer the area of study. In this article, we categorized all selected articles in three groups, on the basis of techniques these used to construct the causal model. To provide a full comparative study under categories of probabilistic, statistical and algebraic approaches, we discussed underlying difficulties, limitations, merits and disadvantages in applying these techniques. The reader will find it helpful to choose and use the appropriate method for a better implication.

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Authors and Affiliations

  1. Electrical and Electronics Engineering Science, University of Johannesburg, Johannesburg, South Africa

    Pramod Kumar Parida & Tshilidzi Marwala

  2. Department of Mathematics, NIT Rourkela, Rourkela, India

    Snehashish Chakraverty

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  1. Pramod Kumar Parida
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  2. Tshilidzi Marwala
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  3. Snehashish Chakraverty
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Correspondence to Pramod Kumar Parida.

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Parida, P.K., Marwala, T. & Chakraverty, S. An Overview of Recent Advancements in Causal Studies. Arch Computat Methods Eng 24, 319–335 (2017). https://doi.org/10.1007/s11831-016-9168-1

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  • DOI: https://doi.org/10.1007/s11831-016-9168-1

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