scholarly journals The Role of Instrumental Variables in Causal Inference Based on Independence of Cause and Mechanism

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 928
Author(s):  
Nataliya Sokolovska ◽  
Pierre-Henri Wuillemin
Keyword(s):  
Causal Inference ◽  
Instrumental Variables ◽  
Latent Variables ◽  
Conditional Independence ◽  
State Of The Art ◽  
Simulated Data ◽  
Research Directions ◽  
Common Causes ◽  
Inference Methods

Causal inference methods based on conditional independence construct Markov equivalent graphs and cannot be applied to bivariate cases. The approaches based on independence of cause and mechanism state, on the contrary, that causal discovery can be inferred for two observations. In our contribution, we pose a challenge to reconcile these two research directions. We study the role of latent variables such as latent instrumental variables and hidden common causes in the causal graphical structures. We show that methods based on the independence of cause and mechanism indirectly contain traces of the existence of the hidden instrumental variables. We derive a novel algorithm to infer causal relationships between two variables, and we validate the proposed method on simulated data and on a benchmark of cause-effect pairs. We illustrate by our experiments that the proposed approach is simple and extremely competitive in terms of empirical accuracy compared to the state-of-the-art methods.

scholarly journals Bianisotropic metasurfaces: physics and applications

Nanophotonics ◽  
2018 ◽  
Vol 7 (6) ◽  
pp. 1069-1094 ◽  
Author(s):  
Viktar S. Asadchy ◽  
Ana Díaz-Rubio ◽  
Sergei A. Tretyakov
Keyword(s):  
State Of The Art ◽  
Surface Current ◽  
Magnetic Surface ◽  
Magnetic Polarization ◽  
Research Directions ◽  
Magnetic Current ◽  
Asymmetric Reflection ◽  
Composite Layers ◽  
Electric And Magnetic Fields

AbstractMetasurfaces as optically thin composite layers can be modeled as electric and magnetic surface current sheets flowing in the layer volume in the metasurface plane. In the most general linear metasurface, the electric surface current can be induced by both incident electric and magnetic fields. Likewise, magnetic polarization and magnetic current can be induced also by external electric field. Metasurfaces which exhibit magnetoelectric coupling are called bianisotropic metasurfaces. In this review, we explain the role of bianisotropic properties in realizing various metasurface devices and overview the state-of-the-art of research in this field. Interestingly, engineered bianisotropic response is seen to be required for realization of many key field transformations, such as anomalous refraction, asymmetric reflection, polarization transformation, isolation, and more. Moreover, we summarize previously reported findings on uniform and gradient bianisotropic metasurfaces and envision novel and prospective research directions in this field.

scholarly journals The Inflation Technique for Causal Inference with Latent Variables

2019 ◽  
Vol 7 (2) ◽  
Author(s):  
Elie Wolfe ◽  
Robert W. Spekkens ◽  
Tobias Fritz
Keyword(s):  
Probability Distribution ◽  
Causal Inference ◽  
Latent Variables ◽  
Causal Model ◽  
Causal Structure ◽  
Bell Inequalities ◽  
Difficult Case ◽  
Common Causes ◽  
Multiple Copies ◽  
New Inequalities

AbstractThe problem of causal inference is to determine if a given probability distribution on observed variables is compatible with some causal structure. The difficult case is when the causal structure includes latent variables. We here introduce the inflation technique for tackling this problem. An inflation of a causal structure is a new causal structure that can contain multiple copies of each of the original variables, but where the ancestry of each copy mirrors that of the original. To every distribution of the observed variables that is compatible with the original causal structure, we assign a family of marginal distributions on certain subsets of the copies that are compatible with the inflated causal structure. It follows that compatibility constraints for the inflation can be translated into compatibility constraints for the original causal structure. Even if the constraints at the level of inflation are weak, such as observable statistical independences implied by disjoint causal ancestry, the translated constraints can be strong. We apply this method to derive new inequalities whose violation by a distribution witnesses that distribution’s incompatibility with the causal structure (of which Bell inequalities and Pearl’s instrumental inequality are prominent examples). We describe an algorithm for deriving all such inequalities for the original causal structure that follow from ancestral independences in the inflation. For three observed binary variables with pairwise common causes, it yields inequalities that are stronger in at least some aspects than those obtainable by existing methods. We also describe an algorithm that derives a weaker set of inequalities but is more efficient. Finally, we discuss which inflations are such that the inequalities one obtains from them remain valid even for quantum (and post-quantum) generalizations of the notion of a causal model.

The Rater Bundle Model

2001 ◽  
Vol 26 (3) ◽  
pp. 283-306 ◽  
Author(s):  
Mark Wilson ◽  
Machteld Hoskens
Keyword(s):  
Item Response ◽  
Conditional Independence ◽  
State Of The Art ◽  
Simulated Data ◽  
Response Model ◽  
Item Response Model ◽  
Student Work ◽  
Response Models ◽  
Item Response Models ◽  
Current State

In this article an item response model is introduced for repeated ratings of student work, which we have called the Rater Bundle Model (RBM). Development of this model was motivated by the observation that when repeated ratings occur, the assumption of conditional independence is violated, and hence current state-of-the-art item response models, such as the rater facets model, that ignore this violation, underestimate measurement error, and overestimate reliability. In the rater bundle model these dependencies are explicitly parameterized. The model is applied to both real and simulated data to illustrate the approach.

Challenges to Inference in the Study of Crisis Bargaining

2015 ◽  
Vol 3 (3) ◽  
pp. 569-587 ◽  
Author(s):  
Philip Arena ◽  
Kyle A. Joyce
Keyword(s):  
At Risk ◽  
Causal Inference ◽  
Instrumental Variables ◽  
Positive Relationship ◽  
Simulated Data ◽  
Bargaining Model ◽  
Data Set ◽  
Crisis Bargaining ◽  
Backwards Induction ◽  
Unobservable Factors

The possibility that actors strategically condition their behavior on partially unobservable factors poses a grave challenge to causal inference, particularly if only some of the actors whose behavior we analyze are at risk of experiencing the outcome of interest. We present a crisis bargaining model that indicates that targets can generally prevent war by arming. We then create a simulated data set where the model is assumed to perfectly describe interactions for those states engaged in crisis bargaining, which we assume most pairs of states arenot. We further assume researchers cannot observe which states are engaged in crisis bargaining, although observable variables might serve as proxies. We demonstrate that a naïve design would falsely (and unsurprisingly) indicate a positive relationship between arming and war. More importantly, we then evaluate the performance of matching, instrumental variables, and statistical backwards induction. The latter two show some promise, but matching fares poorly.

The role of information systems for digital transformation in the private sector: a review of Egyptian SMEs

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Noura Metawa ◽  
Mohamed Elhoseny ◽  
Maha Mutawea
Keyword(s):  
Information Systems ◽  
State Of The Art ◽  
Digital Transformation ◽  
The State ◽  
Future Research ◽  
Research Directions ◽  
Content Type ◽  
The Future ◽  
Full Automation

PurposeThis paper aims to provide insights regarding the state of the art of digital transformation for small- and medium-sized enterprises (SMEs) in Egypt and propose avenues for future research.Design/methodology/approachA proposed framework for the digitization process in SMEs is developed by providing three layers of working steps toward full automation. The paper also provides an extensive analysis of the main requirements for improving the existing traditional information systems' performance in these enterprises. The challenges of digital transformation and the future research direction are discussed as well.FindingsThis paper provided an overview of the importance of digital transformation in real-life applications. The role of the information systems in building a digitalized information processing environment is covered as well. Also, a framework for the shifting process from the traditional approaches to the digitalized systems is proposed. Besides, the paper overviewed the future research directions related to digital transformation in SMEs, especially in Egypt. These research directions are related to technical challenges during the digital transformation process, such as cybersecurity, big data analytics and multimodality data.Originality/valueDespite the significant governmental and institutions' steps toward full automation and digital transformation, the traditional information systems, infrastructures, and unequipped employees make the digitizing process on-the-fly an open challenge. A technology shift that is not supported by a similar cultural change threatens digital business initiatives and increases the risk of their failure. This paper aims to provide insights regarding the state of the art of digital transformation for SMEs in Egypt and propose avenues for future research.

scholarly journals Reflection on modern methods: when worlds collide—prediction, machine learning and causal inference

2019 ◽  
Author(s):  
Tony Blakely ◽  
John Lynch ◽  
Koen Simons ◽  
Rebecca Bentley ◽  
Sherri Rose
Keyword(s):  
Machine Learning ◽  
Causal Inference ◽  
Propensity Scores ◽  
Likelihood Estimation ◽  
Targeted Maximum Likelihood Estimation ◽  
Recent Emergence ◽  
Targeted Maximum Likelihood ◽  
Inference Methods ◽  
Processing Steps

AbstractCausal inference requires theory and prior knowledge to structure analyses, and is not usually thought of as an arena for the application of prediction modelling. However, contemporary causal inference methods, premised on counterfactual or potential outcomes approaches, often include processing steps before the final estimation step. The purposes of this paper are: (i) to overview the recent emergence of prediction underpinning steps in contemporary causal inference methods as a useful perspective on contemporary causal inference methods, and (ii) explore the role of machine learning (as one approach to ‘best prediction’) in causal inference. Causal inference methods covered include propensity scores, inverse probability of treatment weights (IPTWs), G computation and targeted maximum likelihood estimation (TMLE). Machine learning has been used more for propensity scores and TMLE, and there is potential for increased use in G computation and estimation of IPTWs.

scholarly journals Low Molecular Weight Hyaluronic Acid (500–730 Kda) Injections in Tendinopathies—A Narrative Review

2021 ◽  
Vol 7 (1) ◽  
pp. 3
Author(s):  
Antonio Frizziero ◽  
Filippo Vittadini ◽  
Davide Bigliardi ◽  
Cosimo Costantino
Keyword(s):  
Molecular Weight ◽  
Hyaluronic Acid ◽  
State Of The Art ◽  
Clinical Findings ◽  
Molecular Characteristics ◽  
Low Molecular Weight ◽  
Therapeutic Exercises ◽  
Common Causes ◽  
Usual Therapy

Tendinopathies are common causes of pain and disability in general population and athletes. Conservative treatment is largely preferred, and eccentric exercise or other modalities of therapeutic exercises are recommended. However, this approach requests several weeks of consecutive treatment and could be discouraging. In the last years, injections of different formulations were evaluated to accelerate functional recovery in combination with usual therapy. Hyaluronic acid (HA) preparations were proposed, in particular LMW-HA (500–730 kDa) for its unique molecular characteristics in favored extracellular matrix homeostasis and tenocyte viability. The purpose of our review is to evaluate the state-of-the-art about the role of 500–730 kDa in tendinopathies considering both preclinical and clinical findings and encourage further research on this emerging topic.

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