Download or read book Estimating Causal Effects in the Presence of Partial Interference Using Multivariate Bayesian Structural Time Series Models written by Fiammetta Menchetti and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Estimating Causal Effects in the Presence of Spatial Interference written by Keith W. Zirkle and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Environmental epidemiologists are increasingly interested in establishing causality between exposures and health outcomes. A popular model for causal inference is the Rubin Causal Model (RCM), which typically seeks to estimate the average difference in study units' potential outcomes. If the exposure Z is binary, then we may express this as E[Y(Z=1)-Y(Z=0)]. An important assumption under RCM is no interference; that is, the potential outcomes of one unit are not affected by the exposure status of other units. The no interference assumption is violated if we expect spillover or diffusion of exposure effects based on units' proximity to other units and several other causal estimands arise. For example, if we consider the effect of other study units on a unit in an adjacency matrix A, then we may estimate a direct effect, E[Y(Z=1,A)-Y(Z=0,A)], and a spillover effect, E[Y(Z, A)=Y(Z, A`)]. This thesis presents novel methods for estimating causal effects under interference. We begin by outlining the potential outcomes framework and introducing the assumptions necessary for causal inference with no interference. We present an association study that assesses the relationship of animal feeding operations (AFOs) on groundwater nitrate in private wells in Iowa, USA. We then place the relationship in a causal framework where we estimate the causal effects of AFO placement on groundwater nitrate using propensity score-based methods. We proceed to causal inference with interference, which we motivate with examples from air pollution epidemiology where upwind events may affect downwind locations. We adapt assumptions for causal inference in social networks to causal inference with spatially structured interference. We then use propensity score-based methods to estimate both direct and spillover causal effects. We apply these methods to estimate the causal effects of the Environmental Protection Agency's nonattainment regulation for particulate matter on lung cancer incidence in California, Georgia, and Kentucky using data from the Surveillance, Epidemiology, and End Results Program. As an alternative causal method, we motivate use of wind speed as an instrumental variable to define principal strata based on which study units are experiencing interference. We apply these methods to estimate the causal effects of air pollution on asthma incidence in the San Diego, California, USA region using data from the 500 Cities Project. All our methods are proposed in a Bayesian setting. We conclude by discussing the contributions of this thesis and the future of causal analysis in environmental epidemiology.
Download or read book Optimal Nonparametric Estimation of Causal Effects in Clustered Settings written by Chan Park (Ph.D.) and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recently, there has been growing interest in studying causal effects under clustered settings where individual study units can be naturally grouped together. When study units are clustered, data from study units are likely to be dependent of each other and one's potential outcome is affected by others' treatment status; this phenomenon is known as interference in causal inference. The most well-studied type of interference is partial interference where study units are partitioned into non-overlapping clusters and interference only arises within units in the same cluster. Due to the dependencies among units, widely used methodologies to estimate causal effects and optimal treatment rules that are developed under independent and identically distributed data assumption may not be directly applicable in clustered settings. To this end, my research during the doctoral program focuses on estimation of causal effects and optimal treatment rules under partial interference setting. In particular, (i) my research lies in developing flexible, nonparametric methods to infer causal effects in dependent data and showing the optimality of these methods, usually in the form of semiparametric efficiency theory; (ii) my research focuses on partially identifying the causal effects in terms of bounds under a small set of assumptions, as well as demonstrating the statistical properties of the bounding approaches; and (iii) my research interest includes the optimal treatment regime under the presence of interference and dependencies among units using nonparametric methods. In this dissertation presents some of my previous works with additional discussions at the end.
Download or read book Estimating Causal Effects with Matching Methods in the Presence and Absence of Bias Cancellation written by Thomas A. DiPrete and published by . This book was released on 2001 with total page 56 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Estimating Causal Effects with Matching Methods in the Presence and Absence of Bias Cancellation written by and published by . This book was released on with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The Max Planck Institute for Demographic Research presents the full text of the December 2000 working paper entitled "Estimating Causal Effects with Matching Methods in the Presence and Absence of Bias Cancellation," written by Thomas A. DiPrete and Henriette Engelhardt. The text is available in PDF format. This paper uses the Rubin-style of matching methods to examine the implications of possible bias cancellation. The authors show how missing data can complicate the estimation of causal effects.
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 Causality in a Social World written by Guanglei Hong and published by John Wiley & Sons. This book was released on 2015-08-17 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 A Bayesian Semiparametric Multivariate Causal Model with Automatic Covariate Selection and for Possibly Nonignorable Missing Data written by G. Karabatsos and published by . This book was released on 2010 with total page 12 pages. Available in PDF, EPUB and Kindle. Book excerpt: Causal inference is central to educational research, where in data analysis the aim is to learn the causal effects of educational treatments on academic achievement, to evaluate educational policies and practice. Compared to a correlational analysis, a causal analysis enables policymakers to make more meaningful statements about the efficacy of educational treatments. The fundamental problem of causal inference is that, at a given time, each subject can be exposed to only one of the treatments (Holland, 1986). Causal inference becomes inaccurate whenever data violate certain assumptions that are often made in practice, including: (1) the usual assumption of no outliers in the potential outcomes, (2) the typical assumptions that the treatment assignments have no outliers, no hidden bias (e.g., Rosenbaum, 2002), no confounding, and satisfy the Stable Unit Treatment Value Assumption (SUTVA; Cox, 1958); (3) the usual assumption that the missing data values are either missing-at-random (MAR) or missing-completely-at-random (MCAR) (Little & Rubin, 2002; Ibrahim, Chen, Lipsitz, & Herring, 2005), and (4) the usual assumption that parameter estimation requires no penalty for the absolute size of regression coefficients. To address the four open issues of causal modeling, the authors introduce a Bayesian semiparametric causal model, which provides a semiparametric approach to the full Rubin (1978) Causal Model. The paper presents their semiparametric causal model in full detail. The authors then illustrate this model through the analysis of data from the Progress In International Reading Literacy Study (PIRLS), to infer the causal effects of a writing instructional treatment on the reading performance of low-income students. This analysis is performed in a typical context of an observational study where SUTVA is potentially violated by the interference of subjects within each classroom, with many covariates describing the student, teacher, classroom, and school, where hidden bias and confounding can be present, and where there are missing covariate, treatment assignment, and potential outcome data, that can either be randomly (MCAR or MAR) or nonignorably missing. (Contains 3 tables and 3 figures.
Download or read book Estimating Causal Parameters in Marginal Structural Models written by Tanya Amy Henneman and published by . This book was released on 2002 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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 Bayesian Analysis of Stochastic Trends in Structural Time Series Models written by Gary Koop and published by . This book was released on 1998 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper, we make use of state space models to investigate the presence of stochastic trends in economic time series. A model is specified where such a trend can enter either in autoregressive representation or in a separate state equation.
Download or read book Elements of Causal Inference written by Jonas Peters and published by MIT Press. This book was released on 2017-11-29 with total page 289 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Explainable Predictive Modeling and Causal Effect Estimation from Complex Time varying Data written by Tsung Yu Hsieh and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Time-varying data are prevalent in a wide variety of real-world applications for example health care, environmental study, finance, motion capture among others. Time-varying data possess complex nature and pose unique challenges. For example, time-varying data observed in real-world applications almost always exhibit nonstationary characteristics that challenges ordinary time-series methods with stationary assumptions. In addition, one may only have access to irregularly sampled data which prohibits the models that assume regularly observed samples. On the other hand, as machine learning and data mining algorithms have begun make an impact on real-world applications, merely providing accurate prediction is no longer sufficient. There is a growing need for interpretations and explanations to how the machine learning models make predictions in order for end-users to fully trust and adopt these models. In this thesis, we explore time-varying data in various practical scenarios and aim at enhancing model explainability and understanding of the data. First, we study the problem of building explainable classifiers for multivariate time series data by means of joint variable and time interval selection. We introduce a modular framework, the LAXCAT model, consisting of a convolution-based feature extraction and a dual attention mechanism. The convolution-based feature extraction network produces variable-specific representation by considering local time interval context. The dual attention mechanisms, namely variable attention network and temporal attention network, work in concert to simultaneously select variable and time interval that are discriminative to the classification task. We present results of extensive experiments with several benchmark data sets that show that the proposed method outperforms the state-of-the-art baseline methods on multi-variate time series classification task. The results of our case studies demonstrate that the variables and time intervals identified by the proposed method make sense relative to available domain knowledge. Second, to obtain a better understanding of the input multivariate time series data, we study dynamic structure learning which aims at jointly discovering hidden state transitions and state-dependent inter-variable connectivity structures. To address the research problem, we introduce a novel state-regularized dynamic autoregressive model framework, the SrVARM model, featuring a state-regularized recurrent neural network and a dynamic autoregressive model. The state-regularized recurrent unit learns to discover the hidden state transition dynamics from the data while the autoregressive function learns to encode state-dependent inter-variable dependencies in directed acyclic graph structure. A smooth characterization of the acyclic constraint is exploited to train the model in an efficient and unified framework. We report results of extensive experiments with simulated data as well as a real-world benchmark that show that SrVARM outperforms state-of-the-art baselines in recovering the unobserved state transitions and discovering the state-dependent relationships among variables. Third, functional data analysis provides another promising perspective at dealing with time-vary data. However, the representation learning capability of neural network-based method have not been fully explored for functional data. We study unsupervised representation learning from functional data and introduce the functional autoencoder network which generalizes the standard autoencoder network to the functional data setting. The functional autoencoder copes with functional data input by leveraging functional weights and inner product for real-valued functions. We derive from first principles, a functional gradient-based algorithm for training the resulting network. We present results of experiments which demonstrate that the functional autoencoders outperform the state-of-the-art baseline methods. Besides providing a solution to the problem of functional data representation learning, the proposed model offers a fundamental building block for other functional data learning tasks, such as classification and regression networks. Fourth, we study the problem of treatment effect estimation from networked time series data. Such data arise in settings where individuals are linked by a network of relations, e.g., social ties, and the observations for each individual are naturally represented by time series. We propose a novel representation learning approach to treatment effect estimation from networked time series data consisting of a temporal convolution network, a graph attention network, and a treatment-specific outcome predictor network. We use an adversarial learning framework for domain adaptation to learn a representation of individuals that makes treatment assignment independent of the treatment outcome. We present results of experiments and show that the proposed framework outperforms the state-of-the-art baselines in estimating treatment effects from networked time series data. We conclude with a brief summary of the main contributions of the thesis and some directions for further research.
Download or read book Estimating Causal Effects Using Experimental and Observational Design written by Barbara Schneider and published by . This book was released on 2007 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Essays in Causal Inference written by Raiden B. Hasegawa and published by . This book was released on 2019 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt: In observational studies, identifying assumptions may fail, often quietly and without notice, leading to biased causal estimates. Although less of a concern in randomized trials where treatment is assigned at random, bias may still enter the equation through other means. This dissertation has three parts, each developing new methods to address a particular pattern or source of bias in the setting being studied. In the first part, we extend the conventional sensitivity analysis methods for observational studies to better address patterns of heterogeneous confounding in matched-pair designs. We illustrate our method with two sibling studies on the impact of schooling on earnings, where the presence of unmeasured, heterogeneous ability bias is of material concern. The second part develops a modified difference-in-difference design for comparative interrupted time series studies. The method permits partial identification of causal effects when the parallel trends assumption is violated by an interaction between group and history. The method is applied to a study of the repeal of Missouri's permit-to-purchase handgun law and its effect on firearm homicide rates. In the final part, we present a study design to identify vaccine efficacy in randomized control trials when there is no gold standard case definition. Our approach augments a two-arm randomized trial with natural variation of a genetic trait to produce a factorial experiment. The method is motivated by the inexact case definition of clinical malaria.
Download or read book Using Latent Variable Models to Improve Causal Estimation written by Huseyin Oktay and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Estimating the causal effect of a treatment from data has been a key goal for a large number of studies in many domains. Traditionally, researchers use carefully designed randomized experiments for causal inference. However, such experiments can not only be costly in terms of time and money but also infeasible for some causal questions. To overcome these challenges, causal estimation methods from observational data have been developed by researchers from diverse disciplines and increasingly studies using such methods account for a large share in empirical work. Such growing interest has also brought together two arguably separate fields: machine learning and causal estimation, and this thesis also contributes to this intersection. Specifically, in observational data researchers have lack of control over the data generation process. This results in a fundamental challenge: the presence of confounder variables (i.e., variables that affect both treatment and outcome). Such variables, when not adjusted statistically, can result in biased causal estimates. When confounder variables are observed, many methods can be used to adjust for their effect. However, in most real world observational data sets, accurately measuring all potential confounder variables is far from feasible, hence important confounder variables are likely to remain unobserved. The central idea of this thesis is to explicitly account for unobserved confounders by inferring their values using a predictive model. This thesis presents three main contributions in the intersection of machine learning and causal estimation. First, we present one of the earliest application of causal estimation methods from social sciences to social media platforms to answer three causal questions. Second, we present a novel generative model for estimating ordinal variables with distant supervision. We also apply this model to data from US Twitter user population and discover variation in behavior among users from different age groups. Third, we characterize the behavior of an effect restoration model based on graphical models with theoretical analysis and simulation studies. We also apply this effect restoration model with predictive models to account for unobserved confounder variables.
Download or read book Towards Assessing Time Varying Causal Effect Moderation in Experimental and Observational Studies written by Daniel Almirall and published by . This book was released on 2007 with total page 660 pages. Available in PDF, EPUB and Kindle. Book excerpt:
