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Author: Mark J. van der Laan
Publisher: Springer Science & Business Media
ISBN: 1441997822
Category : Mathematics
Languages : en
Pages : 628
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Book Description
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.
Author: Mark J. van der Laan
Publisher: Springer Science & Business Media
ISBN: 1441997822
Category : Mathematics
Languages : en
Pages : 628
Get Book Here
Book Description
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.
Author: Miquel A. Hernan
Publisher: CRC Press
ISBN: 9781420076165
Category : Medical
Languages : en
Pages : 352
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Book Description
The application of causal inference methods is growing exponentially in fields that deal with observational data. Written by pioneers in the field, this practical book presents an authoritative yet accessible overview of the methods and applications of causal inference. With a wide range of detailed, worked examples using real epidemiologic data as well as software for replicating the analyses, the text provides a thorough introduction to the basics of the theory for non-time-varying treatments and the generalization to complex longitudinal data.
Author: Barbara Schneider
Publisher:
ISBN:
Category : Education
Languages : en
Pages : 160
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Book Description
Explains the value of quasi-experimental techniques that can be used to approximate randomized experiments. The goal is to describe the logic of causal inference for researchers and policymakers who are not necessarily trained in experimental and quasi-experimental designs and statistical techniques.
Author: Richard J. Murnane
Publisher: Oxford University Press
ISBN: 0199890153
Category : Psychology
Languages : en
Pages : 414
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Book Description
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.
Author: Judea Pearl
Publisher: Createspace Independent Publishing Platform
ISBN: 9781507894293
Category : Causation
Languages : en
Pages : 0
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Book Description
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.
Author: Mark J. van der Laan
Publisher: Springer
ISBN: 9781441997814
Category : Mathematics
Languages : en
Pages : 628
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Book Description
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.
Author: Jose M. Hernandez
Publisher:
ISBN:
Category :
Languages : en
Pages : 77
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Book Description
This study utilizes a data driven simulation design, which deviates from the traditional model-based approaches most commonly adopted in quasi-experimental Monte Carlo (MC) simulation studies, to answer two main questions. First, this study explores the finite sample properties of the most utilized quasi-experimental methods that control for observable selection bias in the field of education and compares them to traditional regression methods. Second, this study lends an insight into the effects of ignoring the multilevel structure of data commonly found in the field when using quasi-experimental methods. Specifically, treatment effects were estimated using (1) Ordinary Least Squares (OLS) multiple linear regression (treatment effects, adjusted for mean differences on confounders), (2) Propensity Score Matching (PSM) using nearest neighbor 1:n with replacement, (3) Propensity Score Matching using Inverse Probability Weighting (IPW) of the propensity score, and (4) Propensity Score Matching using Sub-classification (Subclassification). There were five main factors that were varied to simulate the data, all of which were fully crossed, as follows: Four sample sizes (600, 1000, 2000, and 5000); three association levels among simulated variables (low, moderate, high); two treatment exposure levels (25% and 50%); four treatment effect sizes using Cohen's d (none, low, moderate, and high); and five levels of ICCs (0, .10, .20, .30, and .40). These 480 conditions were each analyzed with four methods of analysis, for a total of 1920 conditions. Additionally, using data from the Educational Longitudinal Study of 2002 (ELS:2002), an applied study demonstration of the different estimation methods in question was performed and compared to the simulation results. Findings indicate that under certain conditions all methods compared perform the same and have similar estimates of treatment effects. Additionally, when the clustering of the data is ignored bias is introduced for smaller sample size conditions.
Author: Jonas Peters
Publisher: MIT Press
ISBN: 0262037319
Category : Computers
Languages : en
Pages : 289
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Book Description
A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning. The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data. After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems. All of these topics are discussed first in terms of two variables and then in the more general multivariate case. The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for solving multivariate cases. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem. The book is accessible to readers with a background in machine learning or statistics, and can be used in graduate courses or as a reference for researchers. The text includes code snippets that can be copied and pasted, exercises, and an appendix with a summary of the most important technical concepts.
Author: Mark J. van der Laan
Publisher: Springer
ISBN: 3319653040
Category : Mathematics
Languages : en
Pages : 655
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Book Description
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.
Author: Alexander Karl Mayer
Publisher:
ISBN: 9781267029133
Category :
Languages : en
Pages :
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Book Description
Education is among the most important and widely used explanatory covariates in the social sciences, yet we know little about causal effects related to educational advancement or educational policies. Deficits in our knowledge generally derive from the infrequent use of randomized controlled trials and the considerable challenges associated with causal inferences using observational data. This dissertation focuses on two causal questions whose answers have eluded social scientists for nearly fifty years. Both questions involve mechanisms to improve outcomes for the disadvantaged. The obstacles to causal inference for these questions, moreover, are common to questions pertaining to education. Research designs that overcome these obstacles may inform a variety of related research questions. The first question asks whether higher education causes political participation. The second asks whether Title I funding -- additional federal funding for K-12 schools with high concentrations of low-income students -- improves academic performance. Both of these questions have profound implications for politics and public policy in the United States. Individuals with higher education participate in politics at higher rates and consequently have greater influence over political outcomes. Household income correlates highly with children's educational success, and the United States currently spends over $13 billion annually on Title I programs to improve education for low-income students. Recent studies suggest that neither intervention is effective, but this dissertation demonstrates that they neglect to properly evaluate assumptions necessary to support such conclusions. The following chapters re-evaluate the research designs, identify flaws in the studies, demonstrate that the conclusions are premature, and provide evidence for positive effects in each case. Methodologically, this dissertation emphasizes matching and regression discontinuity (RD) designs for causal inference with observational data. These methods are becoming increasingly popular for causal inferences in the social sciences, yet they are not as generally applicable as recent use would imply. This dissertation reviews the identifying assumptions behind each method, and employs a variety of tests to examine the plausibility of these assumptions and provide a firmer foundation for causal inferences. It also identifies flaws in previous applications of these methods to the substantive questions considered here. Moreover, these flaws are not uncommon in the social sciences. In the case of RD applications, few studies that conclude interventions were ineffective consider the statistical power of their designs. Through a series of new analyses using data from three randomized controlled trials, this dissertation also demonstrates how failure to consider statistical power -- a common occurrence in RD applications -- can often lead to incorrect interpretations of null findings. Overall, this dissertation provides evidence for positive effects of higher education on political participation, and for Title I funding on academic performance. This dissertation also demonstrates limits of regression discontinuity designs that have been widely overlooked, and it presents rigorous examples of matching applications -- with detailed post-matching analyses -- to support causal inferences in two important substantive areas at the intersection of education and politics.
