Download or read book Essays on Discrete Multivalued Treatments with Endogeneity and Heterogeneous Counterfactual Errors written by Ibrahim Kekec and published by . This book was released on 2021 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation is composed of three chapters, and each one of them studies discrete multivalued treatments with endogeneity and heterogeneous counterfactual errors. The first chapter extends the investigations of average treatment effects (ATEs) in extensively-studied binary treatments to those in discrete multivalued treatments with both endogeneity and heterogeneous counterfactual errors and explores the behavior of control function (CF) and instrumental variables (IV) methods in this framework. Specifically, I offer identification strategies for the ATEs, suggest a consistent estimator for the ATEs, show the asymptotic properties of CF parameter estimates, and derive a score test in order to draw inferences about the ATEs and other parameters of interest. Moreover, using a Monte Carlo simulation analysis, I compare CF method with widely used IV method in terms of asymptotic efficiency, asymptotic unbiasedness, and consistency. Simulation results suggest that CF method can be asymptotically up to 12% more efficient than IV method, and asymptotic bias in parameter estimates of IV method can be as high as 43%. However, when misspecification is introduced, simulation results favor IV method. For the empirical illustration, I apply ordinary least squares (OLS), CF, IV, and nonparametric bound analysis to the estimation of how limited English proficiency (LEP) influences wages of Hispanic workers in the USA. The data come from the 1% Public Use Microdata Series of the 1990 US Census. Utilizing age at arrival as an instrumental variable, both OLS and CF methods indicate that LEP on average imposes a statistically significant wage penalty (up to 79% in some CF estimates)on Hispanic community in the USA. IV method mostly produces insignificant results, and nonparametric bound analysis provides uninformative lower bounds.The second chapter incorporates a structure of correlated random coefficients (CRCs)into the framework introduced in the first chapter. However, in this new setting with CRCs, conventional IV method is suspected to be inconsistent for ATEs. In this chapter, I propose a consistent CF estimation procedure for the ATEs and show the asymptotic properties of CF parameter estimates. In addition, my Monte Carlo simulation analysis suggests that, in the absence of misspecification, CF method is asymptotically unbiased and consistent (but not necessarily more efficient). Whereas, IV method is generally asymptotically biased and inconsistent. In the presence of misspecification, the simulation results show that both CF and IV methods have biased estimates (more on CF estimates). With regard to efficiency, the simulation findings show that none of the methods outperforms the other one clearly.In the third chapter, I take the treatment model from the first chapter to a specific linear high dimensional sparse setting where the high dimensional variables are irrelevant in treatment choice given the instruments and appear only in the outcome equation. Using a detailed simulation analysis, I examine the finite sample properties, model selection features, and prediction capabilities of several machine learning (ML) methods and of the CF method from the first chapter. To estimate the parameters of interest, I use four different ML methods: LASSO; post partial-out LASSO of Belloni et al. (2012); post double selection LASSO of Belloni, Chernozhukov, and Hansen (2014a); and double/debiased ML LASSO of Chernozhukov et al. (2018). The most important simulation result is that, in the presence of enough extra predictive variables that are ignorable in treatment selection and are from a set of high dimensional predictors of outcome, more complicated LASSO-based methods result inefficiency gains in ATE estimates over the simpler CF method although both LASSO-based methods and the CF method perform more or less the same as far as finite sample bias is concerned. As far as model selection goes, the simulations show that the double/debiased MLLASSO both selects the most number of potential variables and correctly selects the most number of variables with true nonzero impact on outcome in estimation. As to prediction, the simulation results suggest that LASSO has the best prediction features.

Download or read book An Introduction to Causal Inference written by Judea Pearl and published by Createspace Independent Publishing Platform. This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper summarizes recent advances in causal inference and underscores the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underly all causal inferences, the languages used in formulating those assumptions, the conditional nature of all causal and counterfactual claims, and the methods that have been developed for the assessment of such claims. These advances are illustrated using a general theory of causation based on the Structural Causal Model (SCM) described in Pearl (2000a), which subsumes and unifies other approaches to causation, and provides a coherent mathematical foundation for the analysis of causes and counterfactuals. In particular, the paper surveys the development of mathematical tools for inferring (from a combination of data and assumptions) answers to three types of causal queries: (1) queries about the effects of potential interventions, (also called "causal effects" or "policy evaluation") (2) queries about probabilities of counterfactuals, (including assessment of "regret," "attribution" or "causes of effects") and (3) queries about direct and indirect effects (also known as "mediation"). Finally, the paper defines the formal and conceptual relationships between the structural and potential-outcome frameworks and presents tools for a symbiotic analysis that uses the strong features of both. The tools are demonstrated in the analyses of mediation, causes of effects, and probabilities of causation. -- p. 1.

Download or read book Econometric Evaluation of Socio Economic Programs written by Giovanni Cerulli and published by Springer. This book was released on 2015-05-08 with total page 319 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides advanced theoretical and applied tools for the implementation of modern micro-econometric techniques in evidence-based program evaluation for the social sciences. The author presents a comprehensive toolbox for designing rigorous and effective ex-post program evaluation using the statistical software package Stata. For each method, a statistical presentation is developed, followed by a practical estimation of the treatment effects. By using both real and simulated data, readers will become familiar with evaluation techniques, such as regression-adjustment, matching, difference-in-differences, instrumental-variables and regression-discontinuity-design and are given practical guidelines for selecting and applying suitable methods for specific policy contexts.

Download or read book Causal Analysis in Population Studies written by Henriette Engelhardt and published by Springer Science & Business Media. This book was released on 2009-05-05 with total page 253 pages. Available in PDF, EPUB and Kindle. Book excerpt: The central aim of many studies in population research and demography is to explain cause-effect relationships among variables or events. For decades, population scientists have concentrated their efforts on estimating the ‘causes of effects’ by applying standard cross-sectional and dynamic regression techniques, with regression coefficients routinely being understood as estimates of causal effects. The standard approach to infer the ‘effects of causes’ in natural sciences and in psychology is to conduct randomized experiments. In population studies, experimental designs are generally infeasible. In population studies, most research is based on non-experimental designs (observational or survey designs) and rarely on quasi experiments or natural experiments. Using non-experimental designs to infer causal relationships—i.e. relationships that can ultimately inform policies or interventions—is a complex undertaking. Specifically, treatment effects can be inferred from non-experimental data with a counterfactual approach. In this counterfactual perspective, causal effects are defined as the difference between the potential outcome irrespective of whether or not an individual had received a certain treatment (or experienced a certain cause). The counterfactual approach to estimate effects of causes from quasi-experimental data or from observational studies was first proposed by Rubin in 1974 and further developed by James Heckman and others. This book presents both theoretical contributions and empirical applications of the counterfactual approach to causal inference.

Download or read book Causal Inference in Econometrics written by Van-Nam Huynh and published by Springer. This book was released on 2015-12-28 with total page 626 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is devoted to the analysis of causal inference which is one of the most difficult tasks in data analysis: when two phenomena are observed to be related, it is often difficult to decide whether one of them causally influences the other one, or whether these two phenomena have a common cause. This analysis is the main focus of this volume. To get a good understanding of the causal inference, it is important to have models of economic phenomena which are as accurate as possible. Because of this need, this volume also contains papers that use non-traditional economic models, such as fuzzy models and models obtained by using neural networks and data mining techniques. It also contains papers that apply different econometric models to analyze real-life economic dependencies.

Download or read book Collaborative Networks Reference Modeling written by Luis M. Camarinha-Matos and published by Springer Science & Business Media. This book was released on 2008-05-25 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: Collaborative Networks: Reference Modeling works to establish a theoretical foundation for Collaborative Networks. Particular emphasis is put on modeling multiple facets of collaborative networks and establishing a comprehensive modeling framework that captures and structures diverse perspectives of these complex entities. Further, this book introduces a contribution to the definition of reference models for Collaborative Networks. Collaborative Networks: Reference Modeling provides valuable elements for researchers, PhD students, engineers, managers, and leading practitioners interested in collaborative systems and networked society.

Download or read book Targeted Learning in Data Science written by Mark J. van der Laan and published by Springer. This book was released on 2018-03-28 with total page 655 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook for graduate students in statistics, data science, and public health deals with the practical challenges that come with big, complex, and dynamic data. It presents a scientific roadmap to translate real-world data science applications into formal statistical estimation problems by using the general template of targeted maximum likelihood estimators. These targeted machine learning algorithms estimate quantities of interest while still providing valid inference. Targeted learning methods within data science area critical component for solving scientific problems in the modern age. The techniques can answer complex questions including optimal rules for assigning treatment based on longitudinal data with time-dependent confounding, as well as other estimands in dependent data structures, such as networks. Included in Targeted Learning in Data Science are demonstrations with soft ware packages and real data sets that present a case that targeted learning is crucial for the next generation of statisticians and data scientists. Th is book is a sequel to the first textbook on machine learning for causal inference, Targeted Learning, published in 2011. Mark van der Laan, PhD, is Jiann-Ping Hsu/Karl E. Peace Professor of Biostatistics and Statistics at UC Berkeley. His research interests include statistical methods in genomics, survival analysis, censored data, machine learning, semiparametric models, causal inference, and targeted learning. Dr. van der Laan received the 2004 Mortimer Spiegelman Award, the 2005 Van Dantzig Award, the 2005 COPSS Snedecor Award, the 2005 COPSS Presidential Award, and has graduated over 40 PhD students in biostatistics and statistics. Sherri Rose, PhD, is Associate Professor of Health Care Policy (Biostatistics) at Harvard Medical School. Her work is centered on developing and integrating innovative statistical approaches to advance human health. Dr. Rose’s methodological research focuses on nonparametric machine learning for causal inference and prediction. She co-leads the Health Policy Data Science Lab and currently serves as an associate editor for the Journal of the American Statistical Association and Biostatistics.

Download or read book Identification and Inference for Econometric Models written by Donald W. K. Andrews and published by Cambridge University Press. This book was released on 2005-06-17 with total page 606 pages. Available in PDF, EPUB and Kindle. Book excerpt: This 2005 collection pushed forward the research frontier in four areas of theoretical econometrics.

Download or read book Domain Engineering written by Iris Reinhartz-Berger and published by Springer Science & Business Media. This book was released on 2013-08-13 with total page 410 pages. Available in PDF, EPUB and Kindle. Book excerpt: Domain engineering is a set of activities intended to develop, maintain, and manage the creation and evolution of an area of knowledge suitable for processing by a range of software systems. It is of considerable practical significance, as it provides methods and techniques that help reduce time-to-market, development costs, and project risks on one hand, and helps improve system quality and performance on a consistent basis on the other. In this book, the editors present a collection of invited chapters from various fields related to domain engineering. The individual chapters present state-of-the-art research and are organized in three parts. The first part focuses on results that deal with domain engineering in software product lines. The second part describes how domain-specific languages are used to support the construction and deployment of domains. Finally, the third part presents contributions dealing with domain engineering within the field of conceptual modeling. All chapters utilize a similar terminology, which will help readers to understand and relate to the chapters content. The book will be especially rewarding for researchers and students of software engineering methodologies in general and of domain engineering and its related fields in particular, as it contains the most comprehensive and up-to-date information on this topic.

Download or read book Selecting Models from Data written by P. Cheeseman and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 475 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume is a selection of papers presented at the Fourth International Workshop on Artificial Intelligence and Statistics held in January 1993. These biennial workshops have succeeded in bringing together researchers from Artificial Intelligence and from Statistics to discuss problems of mutual interest. The exchange has broadened research in both fields and has strongly encour aged interdisciplinary work. The theme ofthe 1993 AI and Statistics workshop was: "Selecting Models from Data". The papers in this volume attest to the diversity of approaches to model selection and to the ubiquity of the problem. Both statistics and artificial intelligence have independently developed approaches to model selection and the corresponding algorithms to implement them. But as these papers make clear, there is a high degree of overlap between the different approaches. In particular, there is agreement that the fundamental problem is the avoidence of "overfitting"-Le., where a model fits the given data very closely, but is a poor predictor for new data; in other words, the model has partly fitted the "noise" in the original data.

Download or read book Technical and Vocational Education and Training in the Philippines in the Age of Industry 4 0 written by Asian Development Bank and published by Asian Development Bank. This book was released on 2021-03-01 with total page 407 pages. Available in PDF, EPUB and Kindle. Book excerpt: New and emerging technologies under Industry 4.0 are rapidly changing the nature of work and demand for skills around the world. Meanwhile, the coronavirus disease (COVID-19) pandemic is causing significant labor market upheavals. In the Philippines, the impacts on economic growth and employment have been highly disruptive. This publication highlights the vital role technical and vocational education and training (TVET) can play in mitigating the negative impacts of these drivers. It assesses what needs to be done to ensure the country's TVET system, and TESDA, the agency responsible for TVET, can meet the challenges and achieve their objectives of a competitive and socially inclusive workforce.

Download or read book The Foundations of Econometric Analysis written by David F. Hendry and published by Cambridge University Press. This book was released on 1997-02-20 with total page 582 pages. Available in PDF, EPUB and Kindle. Book excerpt: Collection of classic papers by pioneer econometricians

Download or read book Reasoning written by Jonathan E. Adler and published by Cambridge University Press. This book was released on 2008-05-05 with total page 1072 pages. Available in PDF, EPUB and Kindle. Book excerpt: This interdisciplinary work is a collection of major essays on reasoning: deductive, inductive, abductive, belief revision, defeasible (non-monotonic), cross cultural, conversational, and argumentative. They are each oriented toward contemporary empirical studies. The book focuses on foundational issues, including paradoxes, fallacies, and debates about the nature of rationality, the traditional modes of reasoning, as well as counterfactual and causal reasoning. It also includes chapters on the interface between reasoning and other forms of thought. In general, this last set of essays represents growth points in reasoning research, drawing connections to pragmatics, cross-cultural studies, emotion and evolution.

Download or read book Competition Law and Economic Inequality written by Jan Broulík and published by Bloomsbury Publishing. This book was released on 2022-12-15 with total page 379 pages. Available in PDF, EPUB and Kindle. Book excerpt: The gap between the rich and poor is widening across the globe. This book explores whether this major societal challenge of our time can be addressed by the means of competition law. The primary goal of today's competition law is to ensure that market power does not lead to an inefficient production of goods and services. Nevertheless, even such efficiency-oriented curbing of market power may arguably contribute to the reduction of differences in how much people own and earn. Furthermore, many competition law regimes do take into account distributive considerations too. The chapters investigate the relationship between competition law and economic (in)equality from philosophical, historical, and economic perspectives. Their inquiries concern the conceptual foundations of competition law and doctrinal frameworks of individual jurisdictions, as well as specific problems and markets. As such, the book provides a novel and comprehensive overview of whether and how competition law can contribute to more equality in both developed and developing countries. The book is a must-read for researchers, public officials, judges, and practitioners within the competition law community. It will also appeal to anyone more broadly interested in issues of inequality and economic policy.

Download or read book Micro econometrics for Policy Program and Treatment Effects written by Myoung-jae Lee and published by Oxford University Press, USA. This book was released on 2005 with total page 263 pages. Available in PDF, EPUB and Kindle. Book excerpt: In many disciplines of science it is vital to know the effect of a 'treatment' on a response variable of interest; the effect being known as the 'treatment effect'. Here, the treatment can be a drug, an education program or an economic policy, and the response variable can be an illness,academic achievement or GDP. Once the effect is found, it is possible to intervene to adjust the treatment and attain a desired level of the response variable.A basic way to measure the treatment effect is to compare two groups, one of which received the treatment and the other did not. If the two groups are homogenous in all aspects other than their treatment status, then the difference between their response outcomes is the desired treatment effect. Butif they differ in some aspects in addition to the treatment status, the difference in the response outcomes may be due to the combined influence of more than one factor. In non-experimental data where the treatment is not randomly assigned but self-selected, the subjects tend to differ in observedor unobserved characteristics. It is therefore imperative that the comparison be carried out with subjects similar in their characteristics. This book explains how this problem can be overcome so the attributable effect of the treatment can be found.This book brings to the fore recent advances in econometrics for treatment effects. The purpose of this book is to put together various economic treatments effect models in a coherent fashion, make it clear which can be parameters of interest, and show how they can be identified and estimated underweak assumptions. The emphasis throughout the book is on semi- and non-parametric estimation methods, but traditional parametric approaches are also discussed. This book is ideally suited to researchers and graduate students with a basic knowledge of econometrics.

Download or read book Targeted Learning written by Mark J. van der Laan and published by Springer Science & Business Media. This book was released on 2011-06-17 with total page 628 pages. Available in PDF, EPUB and Kindle. Book excerpt: The statistics profession is at a unique point in history. The need for valid statistical tools is greater than ever; data sets are massive, often measuring hundreds of thousands of measurements for a single subject. The field is ready to move towards clear objective benchmarks under which tools can be evaluated. Targeted learning allows (1) the full generalization and utilization of cross-validation as an estimator selection tool so that the subjective choices made by humans are now made by the machine, and (2) targeting the fitting of the probability distribution of the data toward the target parameter representing the scientific question of interest. This book is aimed at both statisticians and applied researchers interested in causal inference and general effect estimation for observational and experimental data. Part I is an accessible introduction to super learning and the targeted maximum likelihood estimator, including related concepts necessary to understand and apply these methods. Parts II-IX handle complex data structures and topics applied researchers will immediately recognize from their own research, including time-to-event outcomes, direct and indirect effects, positivity violations, case-control studies, censored data, longitudinal data, and genomic studies.

Download or read book Applied Bayesian Modeling and Causal Inference from Incomplete Data Perspectives written by Andrew Gelman and published by John Wiley & Sons. This book was released on 2004-09-03 with total page 448 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book brings together a collection of articles on statistical methods relating to missing data analysis, including multiple imputation, propensity scores, instrumental variables, and Bayesian inference. Covering new research topics and real-world examples which do not feature in many standard texts. The book is dedicated to Professor Don Rubin (Harvard). Don Rubin has made fundamental contributions to the study of missing data. Key features of the book include: Comprehensive coverage of an imporant area for both research and applications. Adopts a pragmatic approach to describing a wide range of intermediate and advanced statistical techniques. Covers key topics such as multiple imputation, propensity scores, instrumental variables and Bayesian inference. Includes a number of applications from the social and health sciences. Edited and authored by highly respected researchers in the area.