A Review of Causal Identifiability Techniques across Different Observational Datasets
Journal Title: International Journal of Current Science Research and Review - Year 2023, Vol 6, Issue 11
Abstract
We present an aggregation of the causal identifiability solutions techniques and their assumptions as advanced in extant literatures with datasets of odd origins, which do not necessarily conform to the independent and identically distributed (i.i.d) dataset, multinomial datasets and the Gaussian datasets settings; alongside their concomitant assumptions. The transformation process in data generation can sometimes be a desideratum of datasets of the following forms: linear and non-Gaussian, nonlinear & non-Gaussian, datasets with missing values, datasets tainted with selection biases, datasets with whose variables forms cycles, datasets with heterogeneous/nonstationary variables, datasets with confounding or latent variables, time-series datasets, deterministic datasets, etc. The study begins proper in section 2 after the introduction with the basic background into the concept of causality with observational data. The concept of graph as an embodiment of the background knowledge with structural causal model (SCM) is explicated in section 3; followed by the basic assumptions employed especially with common observational data settings in section 4. An exposition into the categorization of the algorithms used in causality is presented in section 4. Section 5 aggregates and expounds the causal identifiability techniques and their associated assumptions athwart varying datasets; which is the crux of the study and a recapitulation of same is presented in table 1. This study’s main contribution is to present an aggregate review of the causal techniques and their assumptions across different data settings especially in data settings of odd origins, as reviews such as this are grossly lacking in extant literatures.
Authors and Affiliations
Gabriel Terna Ayem, Salu George Thandekkattu, Augustine Shey Nsang,
Keywords
Related Articles
- EP ID EP723010
- DOI 10.47191/ijcsrr/V6-i11-10
- Views 33
- Downloads 0
Adapting the Future VET Curriculum in Response to Emerging Challenges: A Model for Evaluating the Impact of AI Integration in VET for Armed Conflicts and Warfare
This article examines the profound implications and the transformative impact of artificial intelligence (AI) on vocational education and training (VET) within military contexts, underscoring the imperative for curriculu...
Assessing Organizational Restructuring and Crafting Solutions to Manage Workload in Shared Service and Support Unit – PT Telkom Regional III
This study aims to evaluate the impact of organizational restructuring on employee workload and satisfaction in the Shared Service & Support Unit of PT Telkom Regional III. A mixed-method approach was employed, combining...
Synthesis and Characterization of Bis-Acetylacetonatozink (II) [(Zn(acac)2(H2o)]
The synthesis of the bis-acetylacetonate zink (II) monohydrate complex [Zn(acac)2(H2O)] has been conducted. FTIR analysis, TGA-DSC, and SEM micrographic analysis performed complex characterization. The yield of the compl...
Physiological Response of Male Bali Cattle to supplementation of complete pellet feed based on Fermented Corn Cob Waste as a source of fiber and NuPro Yeast Extract Supplement
This study aims to determine the aim of this research, namely to determine the physiological response of male Bali cattle supplemented with complete pelleted feed based on fermented corn cob waste as a source of fiber an...
The Effect of the PJBL Model in Biology Learning on the Learning Outcomes of UPTD SMAN 1 Sarudu Students
The research seeks to elucidate the impact of the PJBL (project based learning) learning paradigm on student academic performance at UPTD SMA Negeri 1 Sarudu. This research utilizes a quantitative approach, specifically...