Download or read book A Bayesian Nonparametric Causal Model for Regression Discontinuity Designs written by George Karabatsos and published by . This book was released on 2013 with total page 8 pages. Available in PDF, EPUB and Kindle. Book excerpt: The regression discontinuity (RD) design (Thistlewaite & Campbell, 1960; Cook, 2008) provides a framework to identify and estimate causal effects from a non-randomized design. Each subject of a RD design is assigned to the treatment (versus assignment to a non-treatment) whenever her/his observed value of the assignment variable equals or exceeds a cutoff value. The RD design provides a "locally-randomized experiment" under remarkably mild conditions, so that the causal effect of treatment outcomes versus non-treatment outcomes can be identified and estimated at the cutoff (Lee, 2008). Such effect estimates are similar to those of a randomized study (Goldberger, 2008/1972). As a result, since 1997, at least 74 RD-based empirical studies have emerged in the ?fields of education, political science, psychology, economics, statistics, criminology, and health science (see van der Klaauw, 2008; Lee & Lemieux, 2010; Bloom, 2012; Wong et al. 2013; Li et al., 2013). Polynomial and local linear models are standard for RD designs (Bloom, 2012; Imbens & Lemieux, 2008). However, these models can produce biased causal effect estimates, due to the presence of outliers of treatment outcomes; and/or due to incorrect choices of the bandwidth parameter for the local linear model. Currently, the correct choice of bandwidth has only been justified by large-sample theory (Imbens & Kalyanaraman, 2012), and the local linear model for quantile regression (Frandsen et al., 2012) suffers from the "quantile crossing" problem. The authors introduce a novel formulation of their Bayesian nonparametric regression model (BLIND, 2012), which provides causal inference for RD designs. It is an infi?nite-mixture model, that allows the entire probability density of the outcome variable to change ?flexibly as a function of the assignment variable. Moreover, the Bayesian model can provide inferences of causal effects, in terms of how the treatment variable impacts the mean, variance, a quantile, distribution function, probability density, hazard function, and/or any other chosen functional of the outcome variable. Moreover, the accurate causal effect estimation relies on a predictively-accurate model for the data. The Bayesian nonparametric regression model attained best overall predictive performance, over many real data sets, compared to many other regression models (BLIND, 2012). Finally, the authors illustrate their Bayesian model through the causal analysis of two real educational data sets. Figures are appended.
Download or read book Bayesian Nonparametrics for Causal Inference and Missing Data written by Michael J. Daniels and published by CRC Press. This book was released on 2023-08-23 with total page 263 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Nonparametrics for Causal Inference and Missing Data provides an overview of flexible Bayesian nonparametric (BNP) methods for modeling joint or conditional distributions and functional relationships, and their interplay with causal inference and missing data. This book emphasizes the importance of making untestable assumptions to identify estimands of interest, such as missing at random assumption for missing data and unconfoundedness for causal inference in observational studies. Unlike parametric methods, the BNP approach can account for possible violations of assumptions and minimize concerns about model misspecification. The overall strategy is to first specify BNP models for observed data and then to specify additional uncheckable assumptions to identify estimands of interest. The book is divided into three parts. Part I develops the key concepts in causal inference and missing data and reviews relevant concepts in Bayesian inference. Part II introduces the fundamental BNP tools required to address causal inference and missing data problems. Part III shows how the BNP approach can be applied in a variety of case studies. The datasets in the case studies come from electronic health records data, survey data, cohort studies, and randomized clinical trials. Features • Thorough discussion of both BNP and its interplay with causal inference and missing data • How to use BNP and g-computation for causal inference and non-ignorable missingness • How to derive and calibrate sensitivity parameters to assess sensitivity to deviations from uncheckable causal and/or missingness assumptions • Detailed case studies illustrating the application of BNP methods to causal inference and missing data • R code and/or packages to implement BNP in causal inference and missing data problems The book is primarily aimed at researchers and graduate students from statistics and biostatistics. It will also serve as a useful practical reference for mathematically sophisticated epidemiologists and medical researchers.
Download or read book Nonparametric Bayesian Inference in Biostatistics written by Riten Mitra and published by Springer. This book was released on 2015-07-25 with total page 448 pages. Available in PDF, EPUB and Kindle. Book excerpt: As chapters in this book demonstrate, BNP has important uses in clinical sciences and inference for issues like unknown partitions in genomics. Nonparametric Bayesian approaches (BNP) play an ever expanding role in biostatistical inference from use in proteomics to clinical trials. Many research problems involve an abundance of data and require flexible and complex probability models beyond the traditional parametric approaches. As this book's expert contributors show, BNP approaches can be the answer. Survival Analysis, in particular survival regression, has traditionally used BNP, but BNP's potential is now very broad. This applies to important tasks like arrangement of patients into clinically meaningful subpopulations and segmenting the genome into functionally distinct regions. This book is designed to both review and introduce application areas for BNP. While existing books provide theoretical foundations, this book connects theory to practice through engaging examples and research questions. Chapters cover: clinical trials, spatial inference, proteomics, genomics, clustering, survival analysis and ROC curve.
Download or read book Bayesian Unimodal Density Regression for Causal Inference written by George Karabatsos and published by . This book was released on 2011 with total page 10 pages. Available in PDF, EPUB and Kindle. Book excerpt: Karabatsos and Walker (2011) introduced a new Bayesian nonparametric (BNP) regression model. Through analyses of real and simulated data, they showed that the BNP regression model outperforms other parametric and nonparametric regression models of common use, in terms of predictive accuracy of the outcome (dependent) variable. The other, outperformed, regression models include random-effects/hierarchical linear and generalized linear models, when the random effects were assumed to be normally-distributed (Laird & Ware, 1982; Breslow & Clayton 1993), and when the random effects were more generally modeled by a nonparametric, Dirichlet process (DP) mixture prior (Kleinman & Ibrahim, 1998a,1998b). The authors argue that the new Bayesian nonparametric (BNP) regression model provides a novel, richer, and more valid approach to causal inference, which allows the researcher to investigate how treatments causally change the entire distribution (density) of (potential) outcomes, including not only the mean, but also other features of the outcome variable, such as quantiles (e.g., median, 10th percentile), and the variance. They illustrate the BNP model through the analysis of observational data, to estimate the causal effect of exposure to excellent high school math education (versus non-exposure, the control), on ACT math achievement. In the data analysis, they also compare the predictive accuracy of the new BNP model against other regression models. These other models assume symmetric distributions for the outcomes, and for the inverse-link function of the propensity score model (when specified), and have been recommended for causal inference from observational data. The other models include the normal linear regression model, having one interaction between (1) subject (pre-treatment) covariates, (2) treatment indicators, and (3) indicators of (greater than or equal) 5 matched groups of subjects, formed either by subclassification (Rosenbaum & Rubin, 1984) or optimal full matching on the estimated propensity score. They also compare with the BART model, which provides a very flexible regression of observed outcomes on the treatment variable and the covariates. Extensive data-based simulation studies have shown that, in terms of bias and mean square error in causal effect estimation, these linear regression models and BART outperform normal linear regression of outcomes using (1) propensity-score-based pair-matching or subclassification alone, (2) treatment indicators and estimated propensity scores as covariates, and (3) observation weights defined by inverse of propensity score estimates, when the only covariate is a treatment indicator (Robins, et al. 2000), and when the linear model also includes subject covariates (Kang & Schafer, 2007; Schafer & Kang, 2008; Hill, 2011). These results seemed to hold true, especially when both the outcome and propensity score models were misspecified for the data, which, arguably, almost always occurs in practice. Through the analysis of an observational data set on math achievement, the authors showed that the new BNP regression model can provide richer causal inferences with higher predictive accuracy, compared to typical causal models which focus inference on the mean outcome, and which make restrictive parametric assumptions about the outcome variable and about the propensity score model. The new BNP model allows one to investigate how treatments causally change any interesting aspect of the distribution (density) of (potential) outcomes, in a flexible manner. (Contains 3 tables and 1 figure.) [This research is supported by the Chicago Teacher Partnership Project.].
Download or read book Bayesian Nonparametric Methods for Causal Inference and Prediction written by Bret Michael Zeldow and published by . This book was released on 2017 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis we present novel approaches to regression and causal inference using popular Bayesian nonparametric methods. Bayesian Additive Regression Trees (BART) is a Bayesian machine learning algorithm in which the conditional distribution is modeled as a sum of regression trees. We extend BART into a semiparametric generalized linear model framework so that a portion of the covariates are modeled nonparametrically using BART and a subset of the covariates have parametric form. This presents an attractive option for research in which only a few covariates are of scientific interest but there are other covariates must be controlled for. Under certain causal assumptions, this model can be used as a structural mean model. We demonstrate this method by examining the effect of initiating certain antiretroviral medications has on mortality among HIV/HCV coinfected subjects. In later chapters, we propose a joint model for a continuous longitudinal outcome and baseline covariates using penalized splines and an enriched Dirichlet process (EDP) prior. This joint model decomposes into local linear mixed models for the outcome given the covariates and marginals for the covariates. The EDP prior that is placed on the regression parameters and the parameters on the covariates induces clustering among subjects determined by similarity in their regression parameters and nested within those clusters, sub-clusters based on similarity in the covariate space. When there are a large number of covariates, we find improved prediction over the same model with Dirichlet process (DP) priors. Since the model clusters based on regression parameters, this model also serves as a functional clustering algorithm where one does not have to choose the number of clusters beforehand. We use the method to estimate incidence rates of diabetes when longitudinal laboratory values from electronic health records are used to augment diagnostic codes for outcome identification. We later extend this work by using our EDP model in a causal inference setting using the parametric g-formula. We demonstrate this using electronic health record data consisting of subjects initiating second generation antipsychotics.
Download or read book Bayesian Nonparametrics for Causal Inference and Missing Data written by Michael Joseph Daniels and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Nonparametrics for Causal Inference and Missing Data provides an overview of flexible Bayesian nonparametric (BNP) methods for modeling joint or conditional distributions and functional relationships, and their interplay with causal inference and missing data. This book emphasizes the importance of making untestable assumptions to identify estimands of interest, such as missing at random assumption for missing data and unconfoundedness for causal inference in observational studies. Unlike parametric methods, the BNP approach can account for possible violations of assumptions and minimize concerns about model misspecification. The overall strategy is to first specify BNP models for observed data and then to specify additional uncheckable assumptions to identify estimands of interest. The book is divided into three parts. Part I develops the key concepts in causal inference and missing data and reviews relevant concepts in Bayesian inference. Part II introduces the fundamental BNP tools required to address causal inference and missing data problems. Part III shows how the BNP approach can be applied in a variety of case studies. The datasets in the case studies come from electronic health records data, survey data, cohort studies, and randomized clinical trials. Features Thorough discussion of both BNP and its interplay with causal inference and missing data How to use BNP and g-computation for causal inference and non-ignorable missingness How to derive and calibrate sensitivity parameters to assess sensitivity to deviations from uncheckable causal and/or missingness assumptions Detailed case studies illustrating the application of BNP methods to causal inference and missing data R code and/or packages to implement BNP in causal inference and missing data problems The book is primarily aimed at researchers and graduate students from statistics and biostatistics. It will also serve as a useful practical reference for mathematically sophisticated epidemiologists and medical researchers.
Download or read book Handbook of Matching and Weighting Adjustments for Causal Inference written by José R. Zubizarreta and published by CRC Press. This book was released on 2023-04-11 with total page 634 pages. Available in PDF, EPUB and Kindle. Book excerpt: An observational study infers the effects caused by a treatment, policy, program, intervention, or exposure in a context in which randomized experimentation is unethical or impractical. One task in an observational study is to adjust for visible pretreatment differences between the treated and control groups. Multivariate matching and weighting are two modern forms of adjustment. This handbook provides a comprehensive survey of the most recent methods of adjustment by matching, weighting, machine learning and their combinations. Three additional chapters introduce the steps from association to causation that follow after adjustments are complete. When used alone, matching and weighting do not use outcome information, so they are part of the design of an observational study. When used in conjunction with models for the outcome, matching and weighting may enhance the robustness of model-based adjustments. The book is for researchers in medicine, economics, public health, psychology, epidemiology, public program evaluation, and statistics who examine evidence of the effects on human beings of treatments, policies or exposures.
Download or read book Regression Discontinuity Designs written by Juan Carlos Escanciano and published by Emerald Group Publishing. This book was released on 2017-05-11 with total page 539 pages. Available in PDF, EPUB and Kindle. Book excerpt: Volume 38 of Advances in Econometrics collects twelve innovative and thought-provoking contributions to the literature on Regression Discontinuity designs, covering a wide range of methodological and practical topics such as identification, interpretation, implementation, falsification testing, estimation and inference.
Download or read book A Nonparametric Bayesian Approach to Causal Modelling written by Tim Henry Guimond and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The Dirichlet process mixture regression (DPMR) method is a technique to produce a very flexible regression model using Bayesian principles based on data clusters. The DPMR method begins by modelling the joint probability density for all variables in a problem. In observational studies, factors which influence treatment assignment (or treatment choice) may also be factors which influence outcomes. In such cases, we refer to these factors as confounders and standard estimates of treatment effects will be biased. Causal modelling approaches allow researchers to make causal inferences from observational data by accounting for confounding variables and thus correcting for the bias in unadjusted models. This thesis develops a fully Bayesian model where the Dirichlet process mixture models the joint distribution of all the variables of interest (confounders, treatment assignment and outcome), and is designed in such a way as to guarantee that this clustering approach adjusts for confounding while also providing a flexible model for outcomes. A local assumption of ignorability is required, as contrasted with the usual global assumption of strong ignorability, and the meaning and consequences of this alternate assumption are explored. The resulting model allows for inferences which are in accordance with causal model principles. In addition to estimating the overall average treatment effect (mean difference between two treatments), it also provides for the determination of conditional outcomes, hence can predict a region of the covariate space where one treatment dominates. Furthermore, the technique's capacity to examine the strongly ignorable assumption is demonstrated. This method can be harnessed to recreate the underlying counterfactual distributions that produce observational data and this is demonstrated with a simulated data set and its results are compared to other common approaches. Finally, the method is applied to a real life data set of an observational study of two possible methods of integrating mental health treatment into the shelter system for homeless men. This analysis of this data demonstrates a situation where treatments have identical outcomes for a subset of the covariate space and a subset of the space where one treatment clearly dominates, thereby informing an individualized patient driven approach to treatment selection.
Download or read book Bayesian Nonparametrics written by J.K. Ghosh and published by Springer Science & Business Media. This book was released on 2006-05-11 with total page 311 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is the first systematic treatment of Bayesian nonparametric methods and the theory behind them. It will also appeal to statisticians in general. The book is primarily aimed at graduate students and can be used as the text for a graduate course in Bayesian non-parametrics.
Download or read book Some Advances in Bayesian Nonparametric Modeling written by Abel Rodriguez and published by LAP Lambert Academic Publishing. This book was released on 2009-03 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian nonparametric and semiparametric mixture models have become extremely popular in the last 10 years because they provide flexibility and interpretability while preserving computational simplicity. This book is a contribution to this growing literature, discussing the design of models for collections of distributions and their application to density estimation and nonparametric regression. All methods introduced in this book are discussed in the context of complex scientific applications in public health, epidemiology and finance.
Download or read book Regression and Other Stories written by Andrew Gelman and published by Cambridge University Press. This book was released on 2020-07-23 with total page 551 pages. Available in PDF, EPUB and Kindle. Book excerpt: A practical approach to using regression and computation to solve real-world problems of estimation, prediction, and causal inference.
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 Causal Inference in Statistics Social and Biomedical Sciences written by Guido W. Imbens and published by Cambridge University Press. This book was released on 2015-04-06 with total page 647 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text presents statistical methods for studying causal effects and discusses how readers can assess such effects in simple randomized experiments.
Download or read book Impact Evaluation written by Markus Frölich and published by Cambridge University Press. This book was released on 2019-03-21 with total page 431 pages. Available in PDF, EPUB and Kindle. Book excerpt: Encompasses the main concepts and approaches of quantitative impact evaluations, used to consider the effectiveness of programmes, policies, projects or interventions. This textbook for economics graduate courses can also serve as a manual for professionals in research institutes, governments, and international organizations.
Download or read book Data Analysis Using Regression and Multilevel Hierarchical Models written by Andrew Gelman and published by Cambridge University Press. This book was released on 2007 with total page 654 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book, first published in 2007, is for the applied researcher performing data analysis using linear and nonlinear regression and multilevel models.
Download or read book Microeconometrics written by A. Colin Cameron and published by Cambridge University Press. This book was released on 2005-05-09 with total page 1058 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
