scholarly journals Counterfactual causal analysis on structured data

2021 ◽  
Author(s):  
swarna paul ◽  
Tauseef Jamal Firdausi ◽  
Saikat Jana ◽  
Arunava Das ◽  
Piyush Nandi
Keyword(s):  
Fundamental Problem ◽  
Business Environment ◽  
Structured Data ◽  
Causal Effects ◽  
High Fidelity ◽  
Causal Relations ◽  
Daunting Task ◽  
Data Graph ◽  
Business Events ◽  
Causal Variables

Data generated in a real-world business environment can be highly connected with intricate relationships among entities. Studying relationships and understanding their dynamics can provide deeper understanding of business events. However, finding important causal relations among entities is a daunting task with heavy dependency on data scientists. Also due to fundamental problem of causal inference it is impossible to directly observe causal effects. Thus, a method is proposed to explain predictive causal relations in an arbitrary linked dataset using counterfactual type causality. The proposed method can generate counterfactual examples with high fidelity in minimal time. It can explain causal relations among any chosen response variable and an arbitrary set of independent causal variables to provide explanations in natural language. The evidence of the explanations is shown in the form of a summarized connected data graph

scholarly journals Counterfactual causal analysis on structured data

2021 ◽  
Author(s):  
swarna paul ◽  
Tauseef Jamal Firdausi ◽  
Saikat Jana ◽  
Arunava Das ◽  
Piyush Nandi
Keyword(s):  
Fundamental Problem ◽  
Business Environment ◽  
Structured Data ◽  
Causal Effects ◽  
High Fidelity ◽  
Causal Relations ◽  
Daunting Task ◽  
Data Graph ◽  
Business Events ◽  
Causal Variables

Data generated in a real-world business environment can be highly connected with intricate relationships among entities. Studying relationships and understanding their dynamics can provide deeper understanding of business events. However, finding important causal relations among entities is a daunting task with heavy dependency on data scientists. Also due to fundamental problem of causal inference it is impossible to directly observe causal effects. Thus, a method is proposed to explain predictive causal relations in an arbitrary linked dataset using counterfactual type causality. The proposed method can generate counterfactual examples with high fidelity in minimal time. It can explain causal relations among any chosen response variable and an arbitrary set of independent causal variables to provide explanations in natural language. The evidence of the explanations is shown in the form of a summarized connected data graph

scholarly journals A Graphical Approximation to Generalization: Definitions and Diagrams

2015 ◽  
Vol 6 (1) ◽  
Author(s):  
Fernando Martel García ◽  
Leonard Wantchekon
Keyword(s):  
External Validity ◽  
Fundamental Problem ◽  
Systematic Variation ◽  
Causal Effects ◽  
Graphical Language ◽  
Causal Diagrams ◽  
Causal Approach

AbstractThe fundamental problem of external validity is not to generalize from one experiment, so much as to experimentally test generalizable theories. That is, theories that explain the systematic variation of causal effects across contexts. Here we show how the graphical language of causal diagrams can be used in this endeavour. Specifically we show how generalization is a causal problem, how a causal approach is more robust than a purely predictive one, and how causal diagrams can be adapted to convey partial parametric information about interactions.

scholarly journals Influences of Unemployment Rates and S&P 500 Movements On Eight Selected U.S. Casino Stock Performances

1970 ◽  
Vol 31 (1) ◽  
pp. 221-240
Author(s):  
Matiur Rahman ◽  
Muhammad Mustafa ◽  
Stephen Caples
Keyword(s):  
Stock Returns ◽  
Causal Effects ◽  
Series Data ◽  
Feedback Effects ◽  
Unemployment Rates ◽  
Short Run ◽  
Interactive Feedback ◽  
Causal Variables

This paper studies the causal effects of changes in unemployment rates andU.S. S&P 500 returns on changes in stock returns of selected eight U.S. casinos individually.Monthly data from January, 1982 through July, 2012 are employed. Thetime series data in percentage changes are found stationary. As a result, multivariateVAR in first-difference is implemented since the objective is to investigate the effectsof changes in causal variables on changes in individual selected casino stockreturns. The estimates depict weakly positive and somewhat mixed causal influencesof changes in unemployment rates on changes in casino stock returns. In the caseof the changes in S&P 500, the results are uniformly positive and relatively strong.In other words, the latter unleash stronger influence than the former on changes incasino stock returns with mixed net short-run interactive feedback effects.

scholarly journals Estimating Causal Effects from Nonparanormal Observational Data

2018 ◽  
Vol 14 (2) ◽  
Author(s):  
Seyed Mahdi Mahmoudi ◽  
Ernst C. Wit
Keyword(s):  
Directed Acyclic Graph ◽  
Large Scale ◽  
Causal Effect ◽  
Causal Effects ◽  
Data Sampling ◽  
Aims Of Science ◽  
Daunting Task ◽  
Large Systems ◽  
Equivalent Systems ◽  
Non Gaussian

AbstractOne of the basic aims of science is to unravel the chain of cause and effect of particular systems. Especially for large systems, this can be a daunting task. Detailed interventional and randomized data sampling approaches can be used to resolve the causality question, but for many systems, such interventions are impossible or too costly to obtain. Recently, Maathuis et al. (2010), following ideas from Spirtes et al. (2000), introduced a framework to estimate causal effects in large scale Gaussian systems. By describing the causal network as a directed acyclic graph it is a possible to estimate a class of Markov equivalent systems that describe the underlying causal interactions consistently, even for non-Gaussian systems. In these systems, causal effects stop being linear and cannot be described any more by a single coefficient. In this paper, we derive the general functional form of a causal effect in a large subclass of non-Gaussian distributions, called the non-paranormal. We also derive a convenient approximation, which can be used effectively in estimation. We show that the estimate is consistent under certain conditions and we apply the method to an observational gene expression dataset of the Arabidopsis thaliana circadian clock system.

Micromechanics-Based Modeling of Biological Tissues Using the High-Fidelity Generalized Method of Cells

2004 ◽  
Vol 844 ◽  
Author(s):  
Marek-Jerzy Pindera
Keyword(s):  
Fundamental Problem ◽  
Biological Tissues ◽  
High Fidelity ◽  
Chordae Tendineae ◽  
Micromechanics Model ◽  
Deformation Regime ◽  
Generalized Method Of Cells ◽  
Infinitesimal Deformations ◽  
The Individual ◽  
Bio Engineering

ABSTRACTThe fundamental problem of micromechanics is the prediction of overall response of a composite material given the properties or response of the individual constituents and their internal geometric arrangement. The recently developed High-Fidelity Generalized Method of Cells is a promising micromechanics model whose predictive capability has been demonstrated for infinitesimal deformations in the presence of inelastic constituent behavior. The extension of this micromechanics model to the finite-deformation regime and incorporation of the quasi-linear viscoelasticity theory for the constituent response extends the range of this model's applicability to the bio-engineering area. Herein, an application involving the response of mitral valve chordae tendineae is presented that demonstrates the model's capability to mimic experimentally-observed response of this class of biological tissues rooted in their characteristic microstructures.

GENERALIZED STATISTICAL COMPLEXITY MEASURE

2010 ◽  
Vol 20 (03) ◽  
pp. 775-785 ◽  
Author(s):  
OSVALDO A. ROSSO ◽  
LUCIANA DE MICCO ◽  
HILDA A. LARRONDO ◽  
MARÍA T. MARTÍN ◽  
ANGEL PLASTINO
Keyword(s):  
Dynamical System ◽  
Time Series ◽  
Probability Distribution ◽  
Metric Space ◽  
Fundamental Problem ◽  
Complexity Measure ◽  
Causal Effects ◽  
Probability Metric ◽  
Statistical Complexity ◽  
Selection Of

A generalized Statistical Complexity Measure (SCM) is a functional that characterizes the probability distribution P associated to the time series generated by a given dynamical system. It quantifies not only randomness but also the presence of correlational structures. We review here several fundamental issues in such a respect, namely, (a) the selection of the information measure [Formula: see text]; (b) the choice of the probability metric space and associated distance [Formula: see text]; (c) the question of defining the so-called generalized disequilibrium [Formula: see text]; (d) the adequate way of picking up the probability distribution P associated to a dynamical system or time series under study, which is indeed a fundamental problem. In this communication we show (point d) that sensible improvements in the final results can be expected if the underlying probability distribution is "extracted" via appropriate consideration regarding causal effects in the system's dynamics.

VSL collaborative online business events

2011 ◽  
Vol 1 (4) ◽  
pp. 1-16
Author(s):  
Roma Chauhan
Keyword(s):  
Decision Making ◽  
Strategic Alliance ◽  
Business Environment ◽  
Subject Area ◽  
Strategic Decision ◽  
Content Type ◽  
Brand Valuation ◽  
Competitive Edge ◽  
Online Business ◽  
Business Events

TitleVSL collaborative online business events.Subject areaThe case is related to strategy of innovation, strategic marketing and brand valuation.Student level/applicabilityThe case consolidates techniques and methodologies of businesses that demonstrate use of technology and innovation to attain competitive edge. It is appropriate for Master's, executive level programme and advance specialized courses of strategy and entrepreneurship. Introductory classes on basics of strategy and information technology will be value add for students.Case overviewIn the growing digital era of virtualization, the businesses are depended on technology to facilitate their multiple operations. Virtual events of conference and exhibition provide broad opportunity to connect and collaborate in real time across the globe. The case discussion applies to potential use of virtual platform as a collaborative tool to achieve business objectives. This case highlights the strategic decision making by an IT company – VSL, regarding product migration and services diversification. It focuses on considering the appropriate strategy of innovation and to make the right decisions.Strategy of innovation and marketing techniques applied by VSL management to sustain in the competitive environment describes the essence of the case. The case is written with the objective to enhance user conceptual understanding through VSL brand valuation and international strategic alliance with 6Connex.Expected learning outcomesThe case familiarises the students with the complexities and challenges involved in a real business environment and put emphasises on the role of played by management for effective decision making. The case helps students to comprehend the relevance of innovation to achieve competitive edge. The case provides an opportunity of exposure to students so that they can understand the key elements of efficient marketing, strategy of innovation and brand valuation. (Elaborate teaching objectives are appended in the teaching note.)Supplementary materialTeaching notes.

scholarly journals Multi-Cause Effect Estimation with Disentangled Confounder Representation

2021 ◽  
Author(s):  
Jing Ma ◽  
Ruocheng Guo ◽  
Aidong Zhang ◽  
Jundong Li
Keyword(s):  
Real World ◽  
Fundamental Problem ◽  
Causal Effect ◽  
Causal Effects ◽  
Treatment Effect Estimation ◽  
Latent Representations ◽  
Real World Datasets ◽  
New Perspective ◽  
Multiple Treatments ◽  
Effect Estimation

One fundamental problem in causality learning is to estimate the causal effects of one or multiple treatments (e.g., medicines in the prescription) on an important outcome (e.g., cure of a disease). One major challenge of causal effect estimation is the existence of unobserved confounders -- the unobserved variables that affect both the treatments and the outcome. Recent studies have shown that by modeling how instances are assigned with different treatments together, the patterns of unobserved confounders can be captured through their learned latent representations. However, the interpretability of the representations in these works is limited. In this paper, we focus on the multi-cause effect estimation problem from a new perspective by learning disentangled representations of confounders. The disentangled representations not only facilitate the treatment effect estimation but also strengthen the understanding of causality learning process. Experimental results on both synthetic and real-world datasets show the superiority of our proposed framework from different aspects.

scholarly journals The Various and Conflicting Notions of Information

10.28945/3154 ◽  
2007 ◽  
Author(s):  
Karl Knox
Keyword(s):  
Business Environment ◽  
Structured Data ◽  
Alternative View ◽  
Epistemological Perspective ◽  
Starting Point ◽  
Human Understanding ◽  
Successful Approach ◽  
The Relationship ◽  
Information And Technology ◽  
Processual Approach

There are identified within the discourse a number of notions regarding the term information. This paper sets out to explore these sometimes-conflicting notions of information. The reason why conflicting notions occur is the result of different perspectives and understanding of the term information. Within the discourse two camps are identified, firstly, those who identify information as a resource and those who identify information as a processual approach enacted by individuals. The former is not uncommon within the business environment given the relationship seen between information and technology; this view simplifies information as merely structured data. The latter approach requires the involvement of individuals or more succinctly human understanding and interpretation. By viewing information as a processual process enacted by humans one is identifying an alternative view of how information is created, managed, used and developed. The aim is to discuss both views to gain clarity and understanding in terms of why the various and conflicting notions of information impact on its use within organisations. What is highlighted within this paper is that information is a complex and ambiguous term. There is no easy ‘off-the shelf solution to managing information. One potentially successful approach is to view information from an epistemological perspective. This requires those having to deal with this complex and ambiguous term a starting point from which to build and gain both an individual and an organisational understanding in terms of the use of information. This allows individuals to set direction, decide where to focus their effort and ultimately how to gain some control over this vital and important issue of ‘information’.

scholarly journals Taxation of Digital Business Transactions: Challenges and Prospects for Developing Economies

2020 ◽  
Vol 6 (3) ◽  
pp. p38
Author(s):  
Jones Adjei Ntiamoah ◽  
Joseph Asare
Keyword(s):  
Rapid Growth ◽  
Global Economy ◽  
Developing Economies ◽  
Business Environment ◽  
Information And Communications Technology ◽  
Technological Advancement ◽  
Goods And Services ◽  
Business Transactions ◽  
Welfare Losses ◽  
Daunting Task

The Global economy has seen a rapid growth in digital business transactions, especially in the provision and sale of goods and services through the application of information and communications technology. This rapid growth can be attributed to the fast rate of technological advancement which has changed how business transactions are conducted globally. The complexity associated with the taxation of digital business transaction has created the need to take a critical look at the challenges and prospects of digital business transactions in developing economies. The absence of physically commercial environment to a digital business environment generates thoughtful and significant issues in relation to taxation and taxation systems. This study relied on the second-best Tax Theory to establish that developing economies face the daunting task of taxing digital business transactions effectively due to the distortive nature of taxes on welfare losses. Whereas government and revenue collection authorities can impose tax on individuals, institutions and or products, they cannot tax digital business transactions efficiently and effectively and unless there are robust and effective tax systems in place. Even though these constraints appear more pronounced in developing economies, the implementation of effective tax could lead to an enhanced internal tax revenue for development.

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