Download or read book Estimation and Testing Methods for Causal Inference with Interference written by Wu Han and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Causal inference is one of the core areas of research in modern data science that allows researchers to determine whether a specific intervention or treatment has an effect on an outcome. In its most basic form, causal inference is concerned with understanding the "cause and effect" relationship between variables. This requires going beyond correlation to understand whether changing one variable leads to a change in another. The gold standard for inferring causality is the randomized controlled trial, which randomly assigns subjects to a treatment or control group and compares outcomes. While randomized controlled trials provide us with data to do causal inference, the subsequent statistical analysis often relies on a key assumption known as the Stable Unit Treatment Value Assumption (SUTVA). This assumption states that the treatment of one unit (or individual) does not affect the outcome of another unit. However, in many real-world situations, this assumption does not hold, leading to what is called interference or a violation of SUTVA. Interference can occur in various contexts such as social networks, where the treatment of one person can influence the outcomes of others, or in marketplace, where treatment of one entity can impact other entities of same type. Understanding and handling interference is a critical and complex aspect of causal inference, and it necessitates more advanced methods to correctly estimate causal effects. This dissertation offers new methodologies and theoretical results to address key issues in causal inference with interference. Specifically, we develop inferential results for causal effect estimators in panel experiments under interference, introduce novel estimation methods for causal effects with network experiments and tackle the problem of detecting interference in online controlled experiments with increasing allocation.

Download or read book Causal Inference Under the K nearest Neighbors Interference Model written by Samirah Alzubaidi and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In causal inference, an experiment exhibits treatment interference when the treatment status of one unit affects the response of other units. While traditional causal inference methods often assume no interference between units, there has been a recent abundance of work on the design and analysis of experiments under treatment interference--- for example, those conducted on social networks. Failure to account for interference may lead to biased estimates of treatment effects and wrong conclusions. In this dissertation, we propose the K-nearest neighbors interference model (KNNIM)---a model of treatment interference where the response of a unit depends only on its treatment status and the statuses of units within its K-neighborhood. Current methods for detecting interference include carefully designed randomized experiments and conditional randomization tests on a set of focal units. We give guidance on how to choose focal units under KNNIM. We then conduct a simulation study to evaluate the efficacy of existing methods for detecting arbitrary network interference under KNNIM with this choice of focal units. We show that this choice of focal units leads to powerful tests of treatment interference which outperform experimental methods. Then, we extend the potential outcomes approach and the K-neighborhood interference framework to define causal estimands for direct and K-nearest neighbors indirect effects where interference is allowed within K-neighborhoods of individuals. Under completely randomized and Bernoulli-randomized designs, we provide a closed-form solution to compute the marginal and joint probabilities of units being exposed to treatment exposures of interest. We then propose Horvitz-Thompson unbiased estimators for the defined estimands under K-neighborhood interference assumption. We derive properties of the proposed estimators and provide conservative variance estimators. We then demonstrate how an assumption of no interaction between direct and indirect effects can improve estimates. To demonstrate the proposed causal methods, we perform a simulation study and apply our proposed methods on an anti-conflict study from a randomized experiment among middle school students in New Jersey. Finally, we develop additional estimators of the defined estimands under an assumption of no interaction between the indirect effects. This may enhance the estimation standard errors by increasing the number of units under this assumption. Properties of the developed estimators are derived as well as conservative variance estimators of the defined estimands.

Download or read book Semi Parametric Estimation in Network Data and Tools for Conducting Complex Simulation Studies in Causal Inference written by Oleg Sofrygin and published by . This book was released on 2016 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation is concerned with application of robust semi-parametric methods to problems of estimation in network-dependent data and the conduct of large-scale simulation studies for causal inference research in epidemiological and medical data. Specifically, Chapter 1 presents a modern semi-parametric approach to estimation of causal effects in a population connected by a single social network. The connectivity of the population units will typically imply that the observed data on these units is no longer independent and identically distributed. Moreover, such social settings typically result in highly dimensional data. This chapter contributes to current statistical methodology by presenting an approach that allows valid estimation and inference and addresses the statistical issues specific to such networked population datasets. The framework of semi-parametric estimation, called the targeted maximum likelihood estimation (TMLE), is presented. This framework improves upon the existing methods by offering robustness, weakened sensitivity to near positivity violations, as well as the ability to deal with high-dimensionality issues of social network data. In particular, this approach relies on the accurate reflection of the background knowledge available for a given scientific problem, allowing estimation and inference without having to make unrealistic assumptions about the structure of the data. In addition, this chapter generalizes previous work describing estimation of complex causal parameters, such as the direct treatment effects under interference and the causal effects of interventions on social network structure. Although the past decade has produced many contributions towards estimation of causal effects in social network settings, there has been considerably less research on the topic of variance estimation for such highly-dependent data. This chapter presents an approach to constructing valid inference, providing a variance estimator that is scalable to very large datasets with highly-connected observations. The efficient open-source software implementation of these methods also accompanies this chapter. Chapter 2 presents open-source software tools for conduct of reproducible simulation studies for complex parameters that emerge from application of causal inference methods in epidemiological and medical research. This simulation software is build on the framework of non-parametric structural equation modeling. This chapter also studies simulation-based testing of statistical methods in causal inference for longitudinal data with time-varying exposure and confounding. It contributes to existing literature by presenting a unified syntax for non-parametrically defining complex causal parameters, which can be used as the model-free and agnostic gold standard for comparison of different statistical methods for causal inference. For instance, this chapter provides various examples of specification and evaluation of causal parameters that arise naturally in longitudinal causal effect analyses when using marginal structural models (MSMs). The application of these newly developed software tools to replication of several previously published simulation studies in causal inference are also described. Chapter 3 builds on the work described in Chapter 2 and addresses the issue of dependent data simulation for causal inference research in social network data. In particular, it provides a model-free approach to test the validity of various estimation procedures in simulated network-settings. This chapter first outlines a non-parametric causal model for units connected by a network and provides various applied examples of simulations with social network data. This chapter also showcases a possible application of the highly scalable open-source software implementation of the semi-parametric estimation methods described in Chapter 1. In particular, a large scale social network simulation study is described, and the performance of three dependent-data estimators from Chapter 1 is examined. This simulation study also examines the problem of inference for network-dependent data, specifically, by comparing the performance of the dependent-data TMLE variance estimator from Chapter 1 to the true TMLE variance derived from simulations. Finally, Chapter 3 concludes with a simulation study of an HIV epidemic described in terms of a longitudinal process which evolves over a static network in discrete time-steps among several highly inter-connected communities. The abstracts of the three works which make up this dissertation are reproduced below. Chapter 1: This chapter describes the robust semi-parametric approach towards estimation and inference for the sample average treatment-specific mean in observational settings where data are collected on a single network of connected units (e.g., in the presence of interference or spillover). Despite recent advances, many of the currently used statistical methods rely on assumption of a specific parametric model for the outcome, even though some of the most important statistical assumptions required by these models are most likely violated in the observational network data settings, resulting in invalid and anti-conservative statistical inference. In this chapter, we rely on the recent methodological advances for the targeted maximum likelihood estimation (TMLE) for data collected on a single population of causally connected units, to describe an estimation approach that permits for more realistic classes of data-generative models and provides valid statistical inference in the context of such network-dependent data. The approach is applied to an observational setting with a single time point stochastic intervention. We start by assuming that the true observed data-generating distribution belongs to a large class of semi-parametric statistical models. We then impose some restrictions on the possible set of the data-generative distributions that may belong to our statistical model. For example, we assume that the dependence among units can be fully described by the known network, and that the dependence on other units can be summarized via some known (but otherwise arbitrary) summary measures. We show that under our modeling assumptions, our estimand is equivalent to an estimand in a hypothetical IID data distribution, where the latter distribution is a function of the observed network data-generating distribution. With this key insight in mind, we show that the TMLE for our estimand in dependent network data can be described as a certain IID data TMLE algorithm, also resulting in a new simplified approach to conducting statistical inference. We demonstrate the validity of our approach in a network simulation study. We also extend prior work on dependent-data TMLE towards estimation of novel causal parameters, e.g., the unit-specific direct treatment effects under interference and the effects of interventions that modify the initial network structure. Chapter 2: This chapter introduces the \pkg{simcausal} \proglang{R} package - an open-source software tool for specification and simulation of complex longitudinal data structures that are based on non-parametric structural equation models. The package aims to provide a flexible tool for simplifying the conduct of transparent and reproducible simulation studies, with a particular emphasis on the types of data and interventions frequently encountered in real-world causal inference problems, such as, observational data with time-dependent confounding, selection bias, and random monitoring processes. The package interface allows for concise expression of complex functional dependencies between a large number of nodes, where each node may represent a measurement at a specific time point. The package allows for specification and simulation of counterfactual data under various user-specified interventions (e.g., static, dynamic, deterministic, or stochastic). In particular, the interventions may represent exposures to treatment regimens, the occurrence or non-occurrence of right-censoring events, or of clinical monitoring events. Finally, the package enables the computation of a selected set of user-specified features of the distribution of the counterfactual data that represent common causal quantities of interest, such as, treatment-specific means, the average treatment effects and coefficients from working marginal structural models. The applicability of \pkg{simcausal} is demonstrated by replicating the results of two published simulation studies. Chapter 3: The past decade has seen an increasing body of literature devoted to the estimation of causal effects in network-dependent data. However, the validity of many classical statistical methods in such data is often questioned. There is an emerging need for objective and practical ways to assess which causal methodologies might be applicable and valid in such novel network-based datasets. In this chapter we describe a set of tools implemented as part of the \pkg{simcausal} \proglang{R} package that allow simulating data based on the non-parametric structural equation model for connected units. We also provide examples of how these simulations may be applied to evaluation of different statistical methods for estimation of causal effects in such data. In particular, these simulation tools are targeted to the types of data and interventions frequently encountered in real-world causal inference research in social networks, such as, observational studies with spill-over or interference. We developed a novel \proglang{R} language interface which simplifies the specification of network-based functional relationships between connected units. Moreover, this network-based syntax can be combined with.

Download or read book Matched Sampling for Causal Effects written by Donald B. Rubin and published by Cambridge University Press. This book was released on 2006-09-04 with total page 5 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Causal Analysis for Generalized Interference Problems written by Chi Zhang and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Causal inference studies the causal relationships between factors by modeling the underlying data generating process. A common goal in causal inference research is to answer what the effects are of the treatments on the outcomes. Traditional causal inference techniques assume data are independent and identically distributed (IID) and thus ignore interactions among single units. However, a unit's treatment may affect another unit's outcome (interference), a unit's treatment may be correlated with another unit's outcome, or a unit's treatment and outcome may be spuriously correlated through another unit. Those unit-level interactions are referred to as generalized interference. To capture such nuances, this work proposes a graphical model, "interaction models," which can model the data generating process of data with generalized interference using causal graphs. In this work, I focus on the estimation of causal effects given data with generalized interference, and use interaction models to conduct a systematic analysis of the bias caused by different types of interactions among units. I start with assuming linearity and present the graphical framework, interaction models. The framework applies to a more general setting where interactions can occur between any units. I derive theorems to detect, quantify, and remove the interaction bias. Those results rely on knowing the exact interaction patterns between units. Next, I show how this assumption can be relaxed and present results for when the exact interaction pattern is unknown, where bounding or unbiasedly estimating the causal effects might be possible. I then show how the interaction model framework and the bias analysis results can be generalized for non-parametric models. Finally, I will discuss a special setting where interactions only occur between separated "blocks," so non-IID data can be reduced to block-IID data.

Download or read book Impact Evaluation written by Markus Fröhlich 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 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 Counterfactuals and Causal Inference written by Stephen L. Morgan and published by Cambridge University Press. This book was released on 2014-11-17 with total page 525 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this second edition of Counterfactuals and Causal Inference, completely revised and expanded, the essential features of the counterfactual approach to observational data analysis are presented with examples from the social, demographic, and health sciences. Alternative estimation techniques are first introduced using both the potential outcome model and causal graphs; after which, conditioning techniques, such as matching and regression, are presented from a potential outcomes perspective. For research scenarios in which important determinants of causal exposure are unobserved, alternative techniques, such as instrumental variable estimators, longitudinal methods, and estimation via causal mechanisms, are then presented. The importance of causal effect heterogeneity is stressed throughout the book, and the need for deep causal explanation via mechanisms is discussed.

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 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.

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 Causal Inference for High Stakes Decisions written by Harsh J. Parikh and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Causal inference methods are commonly used across domains to aid high-stakes decision-making. The validity of causal studies often relies on strong assumptions that might not be realistic in high-stakes scenarios. Inferences based on incorrect assumptions frequently result in sub-optimal decisions with high penalties and long-term consequences. Unlike prediction or machine learning methods, it is particularly challenging to evaluate the performance of causal methods using just the observed data because the ground truth causal effects are missing for all units. My research presents frameworks to enable validation of causal inference methods in one of the following three ways: (i) auditing the estimation procedure by a domain expert, (ii) studying the performance using synthetic data, and (iii) using placebo tests to identify biases. This work enables decision-makers to reason about the validity of the estimation procedure by thinking carefully about the underlying assumptions. Our Learning-to-Match framework is an auditable-and-accurate approach that learns an optimal distance metric for estimating heterogeneous treatment effects. We augment Learning-to-Match framework with pharmacological mechanistic knowledge to study the long-term effects of untreated seizure-like brain activities in critically ill patients. Here, the auditability of the estimator allowed neurologists to qualitatively validate the analysis via a chart-review. We also propose Credence, a synthetic data based framework to validate causal inference methods. Credence simulates data that is stochastically indistinguishable from the observed data while allowing for user-designed treatment effects and selection biases. We demonstrate Credence's ability to accurately assess the relative performance of causal estimation techniques in an extensive simulation study and two real-world data applications. We also discuss an approach to combines experimental and observational studies. Our approach provides a principled approach to test for the violations of key assumptions and estimate causal effects (Chapter 5).

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 Statistical Inference as Severe Testing written by Deborah G. Mayo and published by Cambridge University Press. This book was released on 2018-09-20 with total page 503 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mounting failures of replication in social and biological sciences give a new urgency to critically appraising proposed reforms. This book pulls back the cover on disagreements between experts charged with restoring integrity to science. It denies two pervasive views of the role of probability in inference: to assign degrees of belief, and to control error rates in a long run. If statistical consumers are unaware of assumptions behind rival evidence reforms, they can't scrutinize the consequences that affect them (in personalized medicine, psychology, etc.). The book sets sail with a simple tool: if little has been done to rule out flaws in inferring a claim, then it has not passed a severe test. Many methods advocated by data experts do not stand up to severe scrutiny and are in tension with successful strategies for blocking or accounting for cherry picking and selective reporting. Through a series of excursions and exhibits, the philosophy and history of inductive inference come alive. Philosophical tools are put to work to solve problems about science and pseudoscience, induction and falsification.

Download or read book Improved Methods for Causal Inference and Data Combination written by Heng Shu and published by . This book was released on 2015 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this dissertation, we develop improved estimation of average treatment effect on the treatment (ATT) which achieves double robustness, local efficiency, intrinsic efficiency and sample boundedness, using a calibrated likelihood approach. Moreover, we consider an extension of two-group causal inference problem to a general data combination problem, and develop estimators achieving desirable properties beyond double robustness and local efficiency. The proposed methods are shown, both theoretically and numerically, to be superior in robustness, efficiency or both to various existing estimators. In the first part, we review existing estimators on average treatment effect (ATE), mainly based on Tan (2006, 2010). This review provides a useful basis for improved estimation of average treatment effect on the treated (ATT). In the second part, we propose new methods to estimate the average treatment effect on the treated (ATT), which is of extensive interest in Econometrics, Biostatistics and other research fields. This problem seems to be often treated as a simple modification or extension of that of estimating overall average treatment effects (ATE). But the propensity score is no longer ancillary for estimation of ATT, in contrast with estimation of ATE. We study the efficient influence function and the corresponding semiparametric variance bound for the estimation of ATT under three different assumptions: a nonparametric model, a correct propensity score model and known propensity score. Then we construct Augmented Inverse Probability Weighted (AIPW) estimators which are locally efficient and doubly robust. Furthermore, we develop calibrated regression and likelihood estimators that are not only doubly robust and locally efficient, but also intrinsically e cient and sample bounded. Two simulations and real data analysis on a job training program are provided to demonstrate the advantage of our estimators compared with existing estimators. In the third part, we extend our methods to a general data combination problem for moment restriction models (Chen et al. 2008). Similarly, we derive augmented inverse probability weighted (AIPW) estimators that are locally efficient and doubly robust. Moreover, we develop calibrated regression and likelihood estimators which achieve double robustness, local efficiency and intrinsic efficiency. For illustration, we take the linear two-sample instrumental variable problem as an example, and derive all the relevant estimators by applying the general estimators in this specific example. Finally, a simulation study and an Econometric application on a public housing project are provided to demonstrate the superior performance of our improved estimators.

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 Counterfactuals and Causal Inference written by Stephen L. Morgan and published by Cambridge University Press. This book was released on 2015 with total page 525 pages. Available in PDF, EPUB and Kindle. Book excerpt: This new edition aims to convince social scientists to take a counterfactual approach to the core questions of their fields.