Download or read book Causal Inference with Longitudinal Data Moving Beyond Difference in Difference written by Landon Manzano Gibson and published by . This book was released on 2020 with total page 119 pages. Available in PDF, EPUB and Kindle. Book excerpt: Difference-in-Difference is a widely used method in health policy and health services research for estimating a causal effect. Unfortunately, the validity of difference-in-difference is difficult to evaluate without a tool to directly assess the parallel trends assumption. For example, existing tools indirectly examine the parallel trends assumption using pre-treatment observations. Developments in the methodological literature have given rise to an alternative class of estimators -- Synthetic Controls -- that do not make the parallel trends assumption and to sensitivity analysis tools that provide a novel approach for directly evaluating the parallel trends assumption The first chapter of this dissertation develops guidelines for the use of synthetic control methods alongside difference-in-difference. Synthetic control methods are a valuable tool because they don't assume parallel trends; however, they are not without assumptions of their own. This chapter provides guidance for the utilization of synthetic controls and difference-in-difference and proposes several post-estimation validity analyses to further evaluate the assumptions made by each method. The second chapter examines the effect of Medicaid Expansion on State Medicaid spending. The analysis is done using a subset of states among which the parallel trends assumptions is tenuous. Using a kernel-balanced synthetic control, and the post-estimation analyses introduced in the first chapter, this paper shows no evidence for Medicaid Expansion increasing or decreasing State Medicaid spending over a three-year period. The third chapter extends a suite of sensitivity tools for estimating the sensitivity of difference-in-difference to unobserved time-varying confounders -- parallel trends violations. The tools utilize the explanatory power of observed covariates to estimate how strong unobserved confounders must be to change the conclusions. They not only relax the strict binary nature of classic indirect parallel trends tests but also utilize the post-period outcome data to directly examine the parallel trends assumption.

Download or read book Explanation in Causal Inference written by Tyler J. VanderWeele and published by Oxford University Press, USA. This book was released on 2015 with total page 729 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive examination of methods for mediation and interaction, VanderWeele's book is the first to approach this topic from the perspective of causal inference. Numerous software tools are provided, and the text is both accessible and easy to read, with examples drawn from diverse fields. The result is an essential reference for anyone conducting empirical research in the biomedical or social sciences.

Download or read book Optic written by Beth Ann Griffin and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This tool can help researchers assess how co-occurring policies and confounding can affect the performance of statistical models commonly used in state policy evaluations. Specifically, the tool helps users compare the performance of various causal inference models using their own longitudinal data. Users can select from a variety of simulation options to explore how different state policy evaluation methods perform. Although the tool was initially created to examine data related to opioids, its framework can be used with longitudinal data on any topic. Recent research on difference-in-differences (DID) models revealed issues with traditional DID models, and there has been an explosion of new methods in this area for researchers to consider. Researchers found it difficult to evaluate the relative performance of different causal inference methods using longitudinal outcome data on opioid mortality and opioid prescribing rates; thus, they designed a series of simulations to study the performance of various methods under different scenarios for any type of repeated measures outcome data. The tool's introductory vignette provides a working example of how to use the package. This example uses the sample overdoses dataset provided with the package. Users need the R software environment (version 4.1.0 or above) to use the package.

Download or read book Causal Inference Using Variation in Treatment Over Time written by Xinyao Ji and published by . This book was released on 2017 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis and related research is motivated by my interest in understanding the use of time-varying treatments in causal inference from complex longitudinal data, which play a prominent role in public health, economics, and epidemiology, as well as in biological and medical sciences. Longitudinal data allow the direct study of temporal changes within individuals and across populations, therefore give us the edge to utilize time this important factor to explore causal relationships than static data. There are also a variety challenges that arise in analyzing longitudinal data. By the very nature of repeated measurements, longitudinal data are multivariate in various dimensions and have completed random-error structures, which make many conventional causal assumptions and related statistical methods are not directly applicable. Therefore, new methodologies, most likely data-driven, are always encouraged and sometimes necessary in longitudinal causal inference, as will be seen throughout this thesis.As a result of the various topics explored, this thesis is split into four parts corresponding to three dierent patterns of variation in treatment. The rst pattern is the one-directional change of a binary treatment assignment, meaning that each study participant is only allowed to experience the change from untreated to treated at the staggered time. Such pattern is observed in a novel cluster-randomized design called the stepped-wedge. The second pattern is the arbitrary switching of a binary treatment caused by changes in person-specic characteristics and general time trend. The patterns is the most common thing one would observe in longitudinal data and we develop a method utilizing trends in treatment to account for unmeasured confounding. The third pattern is that the underlying treatment, outcome, covariates are time-continuous, yet are only observed at discrete time points. Instead of modeling cross-sectional and pooled longitudinal data, we take a mechanistic view by modeling reactions among variables using stochastic dierential equations and investigate whether it is possible to draw sensible causal conclusions from discrete measurements.

Download or read book The SAGE Handbook of Regression Analysis and Causal Inference written by Henning Best and published by SAGE. This book was released on 2013-12-20 with total page 425 pages. Available in PDF, EPUB and Kindle. Book excerpt: ′The editors of the new SAGE Handbook of Regression Analysis and Causal Inference have assembled a wide-ranging, high-quality, and timely collection of articles on topics of central importance to quantitative social research, many written by leaders in the field. Everyone engaged in statistical analysis of social-science data will find something of interest in this book.′ - John Fox, Professor, Department of Sociology, McMaster University ′The authors do a great job in explaining the various statistical methods in a clear and simple way - focussing on fundamental understanding, interpretation of results, and practical application - yet being precise in their exposition.′ - Ben Jann, Executive Director, Institute of Sociology, University of Bern ′Best and Wolf have put together a powerful collection, especially valuable in its separate discussions of uses for both cross-sectional and panel data analysis.′ -Tom Smith, Senior Fellow, NORC, University of Chicago Edited and written by a team of leading international social scientists, this Handbook provides a comprehensive introduction to multivariate methods. The Handbook focuses on regression analysis of cross-sectional and longitudinal data with an emphasis on causal analysis, thereby covering a large number of different techniques including selection models, complex samples, and regression discontinuities. Each Part starts with a non-mathematical introduction to the method covered in that section, giving readers a basic knowledge of the method’s logic, scope and unique features. Next, the mathematical and statistical basis of each method is presented along with advanced aspects. Using real-world data from the European Social Survey (ESS) and the Socio-Economic Panel (GSOEP), the book provides a comprehensive discussion of each method’s application, making this an ideal text for PhD students and researchers embarking on their own data analysis.

Download or read book Causality Between the Built Environment and Subjective Wellbeing written by Jerry Chen and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Causality between the built environment and subjective wellbeing has thus far been segmentally explored and partially quantified. We identify two unresolved challenges in the literature. Firstly, a reliance on cross-sectional data produces associative findings. Secondly, a reductive approach to regress aggregate subjective wellbeing on limited and disparate built environment measurements risks significant confounding effects. We address the research gaps by leveraging residential relocation as a natural experiment to investigate the causality between built environment change and subjective wellbeing (measured with composite score of negatively phrased General Health Questionnaire-12 items). Two causal inference methods (difference-in-differences and synthetic control) are applied and compared. The use of the 'Understanding Society' dataset (The UK Household Longitudinal Study, 2009-2019), combined with holistic locational attributes (Area Classification at the Lower Super Output Area level as per the UK Census) for exploring such causality is novel in literature. Specifically, to estimate the wellbeing effects of residential relocation, we compare movers (treatment n=773) to non-movers (control n=4,619). To estimate the effects of built environment change, we compare movers with a change of built environment type (n=506) to those moving into the same built environment type (n=267). Our research design incorporates novel extensions to the canonical forms of both causal inference methods - staggered difference-in-differences and generalised synthetic control methods - to accommodate individual-level data with multiple relocation timepoints.Our results show immediate and enduring positive causal effects of residential relocation, equivalent to an average improvement of 8% in subjective wellbeing level compared to non-movers. Among movers, moving to a different built environment improves subjective wellbeing by 13% compared with moving to the same built environment type. Without a change in built environment type, the positive causal effects become negligible. We find the distress associated with the relocation is transitory, and preliminary evidence that relocation decisions are formed over years and influenced by acute stressors. We hypothesise that residential relocation and built environment change jointly alleviate existing distresses but play different and limited roles in delivering multi-dimensional subjective wellbeing benefits. We believe causal inference has wide application in urban planning research, and the potential to drive adaptive and human-centric policymaking.

Download or read book Analysis of Longitudinal Data written by Peter Diggle and published by Oxford University Press, USA. This book was released on 2013-03-14 with total page 397 pages. Available in PDF, EPUB and Kindle. Book excerpt: This second edition has been completely revised and expanded to become the most up-to-date and thorough professional reference text in this fast-moving area of biostatistics. It contains an additional two chapters on fully parametric models for discrete repeated measures data and statistical models for time-dependent predictors.

Download or read book The Sage Handbook of Sociology of Education written by Mark Berends and published by SAGE Publications Limited. This book was released on 2023-12-06 with total page 958 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Sage Handbook of Sociology of Education is an international and comprehensive groundbreaking text that serves as a touchstone for researchers and scholars interested in exploring the intricate relationships between education and society. Leading sociologists from five different continents examine major topics in sociology from a global perspective. This timely, thought-provoking Handbook features contributions from leading and emerging sociology scholars, who provide their own cultural and historical perspectives on diverse—yet universal—topics; these include educational policy, social stratification, and cross-national research. 39 Chapters delve into the pressing issues faced by our global society, such as the effects of residential mobility on educational outcomes, gender and ethnic inequalities, and the impact of COVID-19 on early childhood education. Readers will gain a multifaceted view of the contours of educational inequality, from various international perspectives and focusing on country differences, as well as recommendations for expanding the practices, programs, and policies that could reduce the rising tide of inequities—especially for populations most at risk. This Handbook offers rich, diverse perspectives on the interplay between education, social inequality, and human rights around the world, making it an invaluable resource for students, researchers, and practitioners across a range of fields, including sociology, education, and social policy. PART 1: Education and Persistent Inequality PART 2: Social & Family Contexts PART 3: Schools & Educational Policy PART 4: Neighborhoods & Community PART 5: Education & Innovation in a Global Context

Download or read book Causality in a Social World written by Guanglei Hong and published by John Wiley & Sons. This book was released on 2015-06-09 with total page 443 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Evaluating Methods to Estimate the Effect of State Laws on Firearm Deaths written by Terry L. Schell and published by . This book was released on 2019-01-17 with total page 112 pages. Available in PDF, EPUB and Kindle. Book excerpt: As part of the RAND Corporation's Gun Policy in America initiative, the authors use simulations to assess the performance of a wide range of statistical models used to estimate the effects of state gun policies on firearm deaths.

Download or read book Longitudinal Data Analysis written by Jason Newsom and published by Routledge. This book was released on 2013-06-19 with total page 407 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides accessible treatment to state-of-the-art approaches to analyzing longitudinal studies. Comprehensive coverage of the most popular analysis tools allows readers to pick and choose the techniques that best fit their research. The analyses are illustrated with examples from major longitudinal data sets including practical information about their content and design. Illustrations from popular software packages offer tips on how to interpret the results. Each chapter features suggested readings for additional study and a list of articles that further illustrate how to implement the analysis and report the results. Syntax examples for several software packages for each of the chapter examples are provided at www.psypress.com/longitudinal-data-analysis. Although many of the examples address health or social science questions related to aging, readers from other disciplines will find the analyses relevant to their work. In addition to demonstrating statistical analysis of longitudinal data, the book shows how to interpret and analyze the results within the context of the research design. The methods covered in this book are applicable to a range of applied problems including short- to long-term longitudinal studies using a range of sample sizes. The book provides non-technical, practical introductions to the concepts and issues relevant to longitudinal analysis. Topics include use of publicly available data sets, weighting and adjusting for complex sampling designs with longitudinal studies, missing data and attrition, measurement issues related to longitudinal research, the use of ANOVA and regression for average change over time, mediation analysis, growth curve models, basic and advanced structural equation models, and survival analysis. An ideal supplement for graduate level courses on data analysis and/or longitudinal modeling taught in psychology, gerontology, public health, human development, family studies, medicine, sociology, social work, and other behavioral, social, and health sciences, this multidisciplinary book will also appeal to researchers in these fields.

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 Causal Inference in Statistics written by Judea Pearl and published by John Wiley & Sons. This book was released on 2016-01-25 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: CAUSAL INFERENCE IN STATISTICS A Primer Causality is central to the understanding and use of data. Without an understanding of cause–effect relationships, we cannot use data to answer questions as basic as "Does this treatment harm or help patients?" But though hundreds of introductory texts are available on statistical methods of data analysis, until now, no beginner-level book has been written about the exploding arsenal of methods that can tease causal information from data. Causal Inference in Statistics fills that gap. Using simple examples and plain language, the book lays out how to define causal parameters; the assumptions necessary to estimate causal parameters in a variety of situations; how to express those assumptions mathematically; whether those assumptions have testable implications; how to predict the effects of interventions; and how to reason counterfactually. These are the foundational tools that any student of statistics needs to acquire in order to use statistical methods to answer causal questions of interest. This book is accessible to anyone with an interest in interpreting data, from undergraduates, professors, researchers, or to the interested layperson. Examples are drawn from a wide variety of fields, including medicine, public policy, and law; a brief introduction to probability and statistics is provided for the uninitiated; and each chapter comes with study questions to reinforce the readers understanding.

Download or read book Moving Beyond Non Informative Prior Distributions Achieving the Full Potential of Bayesian Methods for Psychological Research written by Christoph Koenig and published by Frontiers Media SA. This book was released on 2022-02-01 with total page 197 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Causal Inference and the Comparative Interrupted Time Series Design written by Travis St. Clair and published by . This book was released on 2014 with total page 14 pages. Available in PDF, EPUB and Kindle. Book excerpt: Researchers are increasingly using comparative interrupted time series (CITS) designs to estimate the effects of programs and policies when randomized controlled trials are not feasible. In a simple interrupted time series design, researchers compare the pre-treatment values of a treatment group time series to post-treatment values in order to assess the impact of a treatment, without any comparison group to account for confounding factors. The CITS design is a version of the ITS design in which both a treatment and a comparison group are evaluated both before and after the onset of a treatment. A growing body of literature is employing a within study comparison (WSC) methodology to examine the validity of the CITS model. WSC studies empirically estimate the extent to which a given observational study reproduces the results of a randomized controlled trial (RCT) when both share the same treatment group, and represent a rigorous method of evaluating non-experimental designs using real data. A number of recent within-study comparisons have demonstrated that CITS can produce estimates that are comparable to those from a randomized controlled trial (RCT) in practice. In the St. Clair et al. (2014) application, the authors found that correspondence with the RCT was possible when the CITS model accounted for baseline trends, but that additional time points could actually increase bias when the pre-treatment trend was not modeled correctly. Examination of the pretreatment trends in this data set showed clearly that in at least one of the outcomes the treatment and comparison groups had different slopes in the pretreatment period, and as a result the "parallel trends" assumption often invoked in the difference-in-difference literature was clearly violated. This paper employs a within study comparison (WSC) methodology to examine the performance of two approaches: (1) a more flexible modeling approach, which employs year fixed-effects rather than trying to parametrically model the pretest trend; and (2) match treatment and comparison cases to reduce reliance on modeling the pretreatment trend. The paper then compares the approaches to the performance of the baseline mean and baseline slope models across three datasets. The purpose of this research is two-fold: (1) to examine what approach, if any, works in the unclear functional form case; and (2) to examine the relative superiority of the different approaches across the three datasets in terms of both bias reduction and precision. Tables and figures are appended.

Download or read book The Estimation of Causal Effects by Difference in difference Methods written by Michael Lechner and published by Foundations and Trends(r) in E. This book was released on 2011 with total page 72 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph presents a brief overview of the literature on the difference-in-difference estimation strategy and discusses major issues mainly using a treatment effect perspective that allows more general considerations than the classical regression formulation that still dominates the applied work.

Download or read book Methods Matter written by Richard J. Murnane and published by Oxford University Press. This book was released on 2010-09-15 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: Educational policy-makers around the world constantly make decisions about how to use scarce resources to improve the education of children. Unfortunately, their decisions are rarely informed by evidence on the consequences of these initiatives in other settings. Nor are decisions typically accompanied by well-formulated plans to evaluate their causal impacts. As a result, knowledge about what works in different situations has been very slow to accumulate. Over the last several decades, advances in research methodology, administrative record keeping, and statistical software have dramatically increased the potential for researchers to conduct compelling evaluations of the causal impacts of educational interventions, and the number of well-designed studies is growing. Written in clear, concise prose, Methods Matter: Improving Causal Inference in Educational and Social Science Research offers essential guidance for those who evaluate educational policies. Using numerous examples of high-quality studies that have evaluated the causal impacts of important educational interventions, the authors go beyond the simple presentation of new analytical methods to discuss the controversies surrounding each study, and provide heuristic explanations that are also broadly accessible. Murnane and Willett offer strong methodological insights on causal inference, while also examining the consequences of a wide variety of educational policies implemented in the U.S. and abroad. Representing a unique contribution to the literature surrounding educational research, this landmark text will be invaluable for students and researchers in education and public policy, as well as those interested in social science.