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Author: Chan Park (Ph.D.)
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Recently, there has been growing interest in studying causal effects under clustered settings where individual study units can be naturally grouped together. When study units are clustered, data from study units are likely to be dependent of each other and one's potential outcome is affected by others' treatment status; this phenomenon is known as interference in causal inference. The most well-studied type of interference is partial interference where study units are partitioned into non-overlapping clusters and interference only arises within units in the same cluster. Due to the dependencies among units, widely used methodologies to estimate causal effects and optimal treatment rules that are developed under independent and identically distributed data assumption may not be directly applicable in clustered settings. To this end, my research during the doctoral program focuses on estimation of causal effects and optimal treatment rules under partial interference setting. In particular, (i) my research lies in developing flexible, nonparametric methods to infer causal effects in dependent data and showing the optimality of these methods, usually in the form of semiparametric efficiency theory; (ii) my research focuses on partially identifying the causal effects in terms of bounds under a small set of assumptions, as well as demonstrating the statistical properties of the bounding approaches; and (iii) my research interest includes the optimal treatment regime under the presence of interference and dependencies among units using nonparametric methods. In this dissertation presents some of my previous works with additional discussions at the end.
Author: Chan Park (Ph.D.)
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
Recently, there has been growing interest in studying causal effects under clustered settings where individual study units can be naturally grouped together. When study units are clustered, data from study units are likely to be dependent of each other and one's potential outcome is affected by others' treatment status; this phenomenon is known as interference in causal inference. The most well-studied type of interference is partial interference where study units are partitioned into non-overlapping clusters and interference only arises within units in the same cluster. Due to the dependencies among units, widely used methodologies to estimate causal effects and optimal treatment rules that are developed under independent and identically distributed data assumption may not be directly applicable in clustered settings. To this end, my research during the doctoral program focuses on estimation of causal effects and optimal treatment rules under partial interference setting. In particular, (i) my research lies in developing flexible, nonparametric methods to infer causal effects in dependent data and showing the optimality of these methods, usually in the form of semiparametric efficiency theory; (ii) my research focuses on partially identifying the causal effects in terms of bounds under a small set of assumptions, as well as demonstrating the statistical properties of the bounding approaches; and (iii) my research interest includes the optimal treatment regime under the presence of interference and dependencies among units using nonparametric methods. In this dissertation presents some of my previous works with additional discussions at the end.
Author: Chan Park (Ph.D.)
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
Recently, there has been growing interest in studying causal effects under clustered settings where individual study units can be naturally grouped together. When study units are clustered, data from study units are likely to be dependent of each other and one's potential outcome is affected by others' treatment status; this phenomenon is known as interference in causal inference. The most well-studied type of interference is partial interference where study units are partitioned into non-overlapping clusters and interference only arises within units in the same cluster. Due to the dependencies among units, widely used methodologies to estimate causal effects and optimal treatment rules that are developed under independent and identically distributed data assumption may not be directly applicable in clustered settings. To this end, my research during the doctoral program focuses on estimation of causal effects and optimal treatment rules under partial interference setting. In particular, (i) my research lies in developing flexible, nonparametric methods to infer causal effects in dependent data and showing the optimality of these methods, usually in the form of semiparametric efficiency theory; (ii) my research focuses on partially identifying the causal effects in terms of bounds under a small set of assumptions, as well as demonstrating the statistical properties of the bounding approaches; and (iii) my research interest includes the optimal treatment regime under the presence of interference and dependencies among units using nonparametric methods. In this dissertation presents some of my previous works with additional discussions at the end.
Author: Guanglei Hong
Publisher: John Wiley & Sons
ISBN: 1118332563
Category : Mathematics
Languages : en
Pages : 443
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Book Description
Causality in a Social World introduces innovative new statistical research and strategies for investigating moderated intervention effects, mediated intervention effects, and spill-over effects using experimental or quasi-experimental data. The book uses potential outcomes to define causal effects, explains and evaluates identification assumptions using application examples, and compares innovative statistical strategies with conventional analysis methods. Whilst highlighting the crucial role of good research design and the evaluation of assumptions required for identifying causal effects in the context of each application, the author demonstrates that improved statistical procedures will greatly enhance the empirical study of causal relationship theory. Applications focus on interventions designed to improve outcomes for participants who are embedded in social settings, including families, classrooms, schools, neighbourhoods, and workplaces.
Author: Donald B. Rubin
Publisher: Cambridge University Press
ISBN: 1139458507
Category : Mathematics
Languages : en
Pages : 5
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Book Description
Matched sampling is often used to help assess the causal effect of some exposure or intervention, typically when randomized experiments are not available or cannot be conducted. This book presents a selection of Donald B. Rubin's research articles on matched sampling, from the early 1970s, when the author was one of the major researchers involved in establishing the field, to recent contributions to this now extremely active area. The articles include fundamental theoretical studies that have become classics, important extensions, and real applications that range from breast cancer treatments to tobacco litigation to studies of criminal tendencies. They are organized into seven parts, each with an introduction by the author that provides historical and personal context and discusses the relevance of the work today. A concluding essay offers advice to investigators designing observational studies. The book provides an accessible introduction to the study of matched sampling and will be an indispensable reference for students and researchers.
Author: Matias D. Cattaneo
Publisher: Cambridge University Press
ISBN: 1009441914
Category : Political Science
Languages : en
Pages : 135
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Book Description
In this Element, which continues our discussion in Foundations, the authors provide an accessible and practical guide for the analysis and interpretation of Regression Discontinuity (RD) designs that encourages the use of a common set of practices and facilitates the accumulation of RD-based empirical evidence. The focus is on extensions to the canonical sharp RD setup that we discussed in Foundations. The discussion covers (i) the local randomization framework for RD analysis, (ii) the fuzzy RD design where compliance with treatment is imperfect, (iii) RD designs with discrete scores, and (iv) and multi-dimensional RD designs.
Author: Joseph Y. Halpern
Publisher: MIT Press
ISBN: 0262035022
Category : Computers
Languages : en
Pages : 240
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Book Description
Explores actual causality, and such related notions as degree of responsibility, degree of blame, and causal explanation. The goal is to arrive at a definition of causality that matches our natural language usage and is helpful, for example, to a jury deciding a legal case, a programmer looking for the line of code that cause some software to fail, or an economist trying to determine whether austerity caused a subsequent depression.
Author: Peter Müller
Publisher: Springer
ISBN: 3319189689
Category : Mathematics
Languages : en
Pages : 203
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Book Description
This book reviews nonparametric Bayesian methods and models that have proven useful in the context of data analysis. Rather than providing an encyclopedic review of probability models, the book’s structure follows a data analysis perspective. As such, the chapters are organized by traditional data analysis problems. In selecting specific nonparametric models, simpler and more traditional models are favored over specialized ones. The discussed methods are illustrated with a wealth of examples, including applications ranging from stylized examples to case studies from recent literature. The book also includes an extensive discussion of computational methods and details on their implementation. R code for many examples is included in online software pages.
Author: Estela Bee Dagum
Publisher: Springer
ISBN: 3319318225
Category : Business & Economics
Languages : en
Pages : 293
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Book Description
This book explores widely used seasonal adjustment methods and recent developments in real time trend-cycle estimation. It discusses in detail the properties and limitations of X12ARIMA, TRAMO-SEATS and STAMP - the main seasonal adjustment methods used by statistical agencies. Several real-world cases illustrate each method and real data examples can be followed throughout the text. The trend-cycle estimation is presented using nonparametric techniques based on moving averages, linear filters and reproducing kernel Hilbert spaces, taking recent advances into account. The book provides a systematical treatment of results that to date have been scattered throughout the literature. Seasonal adjustment and real time trend-cycle prediction play an essential part at all levels of activity in modern economies. They are used by governments to counteract cyclical recessions, by central banks to control inflation, by decision makers for better modeling and planning and by hospitals, manufacturers, builders, transportation, and consumers in general to decide on appropriate action. This book appeals to practitioners in government institutions, finance and business, macroeconomists, and other professionals who use economic data as well as academic researchers in time series analysis, seasonal adjustment methods, filtering and signal extraction. It is also useful for graduate and final-year undergraduate courses in econometrics and time series with a good understanding of linear regression and matrix algebra, as well as ARIMA modelling.
Author: Clark Glymour
Publisher: Academic Press
ISBN: 148326579X
Category : Social Science
Languages : en
Pages : 413
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Book Description
Discovering Causal Structure: Artificial Intelligence, Philosophy of Science, and Statistical Modeling provides information pertinent to the fundamental aspects of a computer program called TETRAD. This book discusses the version of the TETRAD program, which is designed to assist in the search for causal explanations of statistical data. or alternative models. This text then examines the notion of applying artificial intelligence methods to problems of statistical model specification. Other chapters consider how the TETRAD program can help to find god alternative models where they exist, and how it can help detect the existence of important neglected variables. This book discusses as well the procedures for specifying a model or models to account for non-experimental or quasi-experimental data. The final chapter presents a description of the format of input files and a description of each command. This book is a valuable resource for social scientists and researchers.
Author: A. Colin Cameron
Publisher: Cambridge University Press
ISBN: 1139444867
Category : Business & Economics
Languages : en
Pages : 1058
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Book Description
This book provides the most comprehensive treatment to date of microeconometrics, the analysis of individual-level data on the economic behavior of individuals or firms using regression methods for cross section and panel data. The book is oriented to the practitioner. A basic understanding of the linear regression model with matrix algebra is assumed. The text can be used for a microeconometrics course, typically a second-year economics PhD course; for data-oriented applied microeconometrics field courses; and as a reference work for graduate students and applied researchers who wish to fill in gaps in their toolkit. Distinguishing features of the book include emphasis on nonlinear models and robust inference, simulation-based estimation, and problems of complex survey data. The book makes frequent use of numerical examples based on generated data to illustrate the key models and methods. More substantially, it systematically integrates into the text empirical illustrations based on seven large and exceptionally rich data sets.
