Download or read book Robust Causal Learning for the Estimation of Average Treatment Effects written by Yiyan Huang and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many practical decision-making problems in economics and healthcare seek to estimate the average treatment effect (ATE) from observational data. The Double/Debiased Machine Learning (DML) is one of the prevalent methods to estimate ATE in the observational study. However, the DML estimators can suffer an error-compounding issue and even give an extreme estimate when the propensity scores are misspecified or very close to 0 or 1. Previous studies have overcome this issue through some empirical tricks such as propensity score trimming, yet none of the existing literature solves this problem from a theoretical standpoint. In this paper, we propose a Robust Causal Learning (RCL) method to offset the deficiencies of the DML estimators. Theoretically, the RCL estimators i) are as consistent and doubly robust as the DML estimators, and ii) can get rid of the error-compounding issue. Empirically, the comprehensive experiments show that i) the RCL estimators give more stable estimations of the causal parameters than the DML estimators, and ii) the RCL estimators outperform the traditional estimators and their variants when applying different machine learning models on both simulation and benchmark datasets.

Download or read book Robust Causal Estimation of Average Treatment on the Treated with Survival Data written by 鄧嘉萱 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 Developing a Protocol for Observational Comparative Effectiveness Research A User s Guide written by Agency for Health Care Research and Quality (U.S.) and published by Government Printing Office. This book was released on 2013-02-21 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: This User’s Guide is a resource for investigators and stakeholders who develop and review observational comparative effectiveness research protocols. It explains how to (1) identify key considerations and best practices for research design; (2) build a protocol based on these standards and best practices; and (3) judge the adequacy and completeness of a protocol. Eleven chapters cover all aspects of research design, including: developing study objectives, defining and refining study questions, addressing the heterogeneity of treatment effect, characterizing exposure, selecting a comparator, defining and measuring outcomes, and identifying optimal data sources. Checklists of guidance and key considerations for protocols are provided at the end of each chapter. The User’s Guide was created by researchers affiliated with AHRQ’s Effective Health Care Program, particularly those who participated in AHRQ’s DEcIDE (Developing Evidence to Inform Decisions About Effectiveness) program. Chapters were subject to multiple internal and external independent reviews. More more information, please consult the Agency website: www.effectivehealthcare.ahrq.gov)

Download or read book Robust Causal Estimation of Average Treatment with Survival Data written by 阮均雅 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 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 Deep Learning Based Causal Inference for Large Scale Combinatorial Experiments written by Zikun Ye and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Large-scale online platforms launch hundreds of randomized experiments (a.k.a. A/B tests) every day to iterate their operations and marketing strategies, while the combinations of these treatments are typically not exhaustively tested. It triggers an important question of both academic and practical interests: Without observing the outcomes of all treatment combinations, how to estimate the causal effect of any treatment combination and identify the optimal treatment combination? We develop a novel framework combining deep learning and doubly robust estimation to estimate the causal effect of any treatment combination for each user on the platform when observing only a small subset of treatment combinations. Our proposed framework (called debiased deep learning, DeDL) exploits Neyman orthogonality and combines interpretable and flexible structural layers in deep learning. We prove theoretically that this framework yields efficient, consistent, and asymptotically normal estimators under mild assumptions, thus allowing for identifying the best treatment combination when only observing a few combinations. To empirically validate our method, we then collaborate with a large-scale video-sharing platform and implement our framework for three experiments involving three treatments where each combination of treatments is tested. When only observing a subset of treatment combinations, our DeDL approach significantly outperforms other benchmarks to accurately estimate and infer the average treatment effect (ATE) of any treatment combination, and to identify the optimal treatment combination.

Download or read book Robust Estimation for Average Treatment Effects written by Jonathan B. Hill and published by . This book was released on 2013 with total page 42 pages. Available in PDF, EPUB and Kindle. Book excerpt: We study the probability tail properties of the Inverse Probability Weighting (IPW) estimators of the Average Treatment Effect T when there is limited overlap in the covariate distribution. Our main contribution is a new robust estimator that performs substantially better than existing IPW estimators. In the literature either the propensity score is assumed bounded away from 0 and 1, or a fixed or shrinking sample portion of the random variable Z that identifies the average treatment effect by E[Z] = T is trimmed when covariate values are large. In a general setting we propose an asymptotically normal estimator that negligibly trims Z adaptively by its large values which sidesteps dimensionality, bias and poor correspondence properties associated with trimming by the covariates, and provides a simple solution to the typically ad hoc choice of trimming threshold. The estimator is asymptotically normal and unbiased whether there is limited overlap or not. In the event there is only one covariate, we also propose an improved robust IPW estimator that trims when the covariate is large. We then work within a latent variable model of the treatment assignment and characterize the probability tail decay of Z. We show when Z exhibits power law tail decay due to limited overlap, and when it has an infinite variance in which case existing estimators do not necessarily have a Gaussian distribution limit. We demonstrate the tail decay property of Z, and study the tail-trimmed estimators by Monte Carlo experiments. We show that our estimator has lower bias and mean-squared-error, and is closer to normal than an existing robust IPW estimator in its suggested form, and in the improved form we propose here.

Download or read book Robust Causal Inference and Machine Learning with Clinical Applications written by Steven Yadlowsky and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: As healthcare data becomes increasingly ubiquitous, improving data-driven biomedical research is timely and important. There is a rush to learn from these new sources of data, and to implement research findings into clinical practice. While machine learning methods provide compelling examples of recognizing sophisticated patterns in data, their impact rests heavily on their ability to use data to influence decision making, especially in healthcare. The relationship between machine learning and decision making becomes particularly clear through the lens of causal inference. In general, the harm and benefit attributed to a medical decision depends on the causal treatment effect of the decision in the appropriate population, beyond their baseline risk of poor outcomes. In precision medicine research, the goal is to develop treatment decisions for individual patients by considering the sub-population of individuals with similar covariates to each patient. This thesis advances methodology and practice for applying machine learning to learn better decision-making rules that influence clinical practice, and understanding the fundamental possibilities and limitations of using data to learn to make optimal decisions. First, we develop an approach for personalized treatment effect estimation based on the relative ratio of treatment outcomes. Second, we study when we can trust causal results learned from data, and develop a sensitivity analysis for conditional and average treatment effects to bound the bias created from unobserved confounding. Third, noting that treatment benefit is highly correlated with baseline risk for preventative treatments for atherosclerotic cardiovascular disease (ASCVD), we use machine learning approaches to improve ASCVD risk predictions from longitudinal cohort data that affect clinical prescribing practice, particularly among under-represented minorities.

Download or read book Robust Inference on Average Treatment Effects with Possibly More Covariates Than Observations written by Max Farrell and published by . This book was released on 2015 with total page 86 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper concerns robust inference on average treatment effects following model selection. In the selection on observables framework, we show how to construct confidence intervals based on a doubly-robust estimator that are robust to model selection errors and prove that they are valid uniformly over a large class of treatment effect models. The class allows for multivalued treatments with heterogeneous effects (in observables), general heteroskedasticity, and selection amongst (possibly) more covariates than observations. Our estimator attains the semiparametric efficiency bound under appropriate conditions. Precise conditions are given for any model selector to yield these results, and we show how to combine data-driven selection with economic theory. For implementation, we give a specific proposal for selection based on the group lasso and derive new technical results for high-dimensional, sparse multinomial logistic regression. A simulation study shows our estimator performs very well in finite samples over a wide range of models. Revisiting the National Supported Work demonstration data, our method yields accurate estimates and tight confidence intervals.

Download or read book An OLS Based Method for Causal Inference in Observational Studies written by Yuanfang Xu and published by . This book was released on 2019 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: Observational data are frequently used for causal inference of treatment effects on prespecified outcomes. Several widely used causal inference methods have adopted the method of inverse propensity score weighting (IPW) to alleviate the in uence of confounding. However, the IPW-type methods, including the doubly robust methods, are prone to large variation in the estimation of causal e ects due to possible extreme weights. In this research, we developed an ordinary least-squares (OLS)-based causal inference method, which does not involve the inverse weighting of the individual propensity scores. We first considered the scenario of homogeneous treatment effect. We proposed a two-stage estimation procedure, which leads to a model-free estimator of average treatment effect (ATE). At the first stage, two summary scores, the propensity and mean scores, are estimated nonparametrically using regression splines. The targeted ATE is obtained as a plug-in estimator that has a closed form expression. Our simulation studies showed that this model-free estimator of ATE is consistent, asymptotically normal and has superior operational characteristics in comparison to the widely used IPW-type methods. We then extended our method to the scenario of heterogeneous treatment effects, by adding in an additional stage of modeling the covariate-specific treatment effect function nonparametrically while maintaining the model-free feature, and the simplicity of OLS-based estimation. The estimated covariate-specific function serves as an intermediate step in the estimation of ATE and thus can be utilized to study the treatment effect heterogeneity. We discussed ways of using advanced machine learning techniques in the proposed method to accommodate high dimensional covariates. We applied the proposed method to a case study evaluating the effect of early combination of biologic & non-biologic disease-modifying antirheumatic drugs (DMARDs) compared to step-up treatment plan in children with newly onset of juvenile idiopathic arthritis disease (JIA). The proposed method gives strong evidence of significant effect of early combination at 0:05 level. On average early aggressive use of biologic DMARDs leads to around 1:2 to 1:7 more reduction in clinical juvenile disease activity score at 6-month than the step-up plan for treating JIA.

Download or read book Bootstrapping written by Christopher Z. Mooney and published by SAGE. This book was released on 1993-08-09 with total page 84 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book is. . . clear and well-written. . . anyone with any interest in the basis of quantitative analysis simply must read this book. . . . well-written, with a wealth of explanation. . ." --Dougal Hutchison in Educational Research Using real data examples, this volume shows how to apply bootstrapping when the underlying sampling distribution of a statistic cannot be assumed normal, as well as when the sampling distribution has no analytic solution. In addition, it discusses the advantages and limitations of four bootstrap confidence interval methods--normal approximation, percentile, bias-corrected percentile, and percentile-t. The book concludes with a convenient summary of how to apply this computer-intensive methodology using various available software packages.

Download or read book PRICAI 2022 Trends in Artificial Intelligence written by Sankalp Khanna and published by Springer Nature. This book was released on 2022-11-03 with total page 562 pages. Available in PDF, EPUB and Kindle. Book excerpt: This three-volume set, LNAI 13629, LNAI 13630, and LNAI 13631 constitutes the thoroughly refereed proceedings of the 19th Pacific Rim Conference on Artificial Intelligence, PRICAI 2022, held in Shangai, China, in November 10–13, 2022. The 91 full papers and 39 short papers presented in these volumes were carefully reviewed and selected from 432 submissions. PRICAI covers a wide range of topics in the areas of social and economic importance for countries in the Pacific Rim: artificial intelligence, machine learning, natural language processing, knowledge representation and reasoning, planning and scheduling, computer vision, distributed artificial intelligence, search methodologies, etc.

Download or read book Estimating Average Treatment Effects With Propensity Scores Estimated With Four Machine Learning Procedures written by Kip Brown and published by . This book was released on 2018 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt: Background: The increased availability of claims data allows one to build high dimensional datasets, rich in covariates, for accurately estimating treatment effects in medical and epidemiological cohort studies. This paper shows the full potential of machine learning for the estimation of average treatment effects with propensity score methods in a context rich and high dimensional datasets. Methods: Four different methods are used to estimate average treatment effects in the context of time to event outcomes. The four methods explored in this study are LASSO, Random Forest, Gradient Descent Boosting and Artificial Neural networks. Simulations based on an actual medical claims data set are used to assess the efficiency of these methods. The simulations are performed with over 100, 000 observations and 1,100 explanatory variables. Each method is tested on 500 datasets that are created from the original dataset, allowing us to report the mean and standard deviation of estimated average treatment effects. Results: The results are very promising for all four methods; however, LASSO, Random Forest and Gradient Boosting seem to be performing better than Random Forest. Conclusion: Machine Learning methods can be helpful for observational studies that use the propensity score when a very large number of covariates are available, the total number of observations is large, and the dependent event rare. This is an important result given the availability of big data related to Health Economics and Outcomes Research (HEOR) around the world.

Download or read book Causal Inference with Selection and Confounding Variables written by Linbo Wang and published by . This book was released on 2016 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: Most complex observational and randomized studies are motivated by the potential of drawing causal statements. However, a usual statistical analysis may yield estimates that do not have causal interpretations. In fact, unlike most other parameters, identification of causal parameters usually relies on untestable assumptions. Moreover, even under these identification assumptions, estimation of causal parameters often relies on nuisance models. The parameter estimation in the nuisance models is crucial to obtain robust causal effect estimates. My research attempts to address these methodological hallenges. In Chapter 2 we study robust estimation of propensity score weights. The propensity score plays a central role in inferring causal effects from observational studies. In particular, weighting and subclassification are two principal approaches to estimate the average causal effect based on estimated propensity scores. Unlike the conventional version of the propensity score subclassification estimator, if the propensity score model is correctly specified, the weighting methods offer consistent and possibly efficient estimation of the average causal effect. However, this theoretical appeal may be diminished in practice by sensitivity to misspecification of the propensity score model. In contrast, subclassification methods are usually more robust to model misspecification. We hence propose to use subclassification for robust estimation of propensity score weights. Our approach is based on the intuition that the inverse probability weighting estimator can be seen as the limit of subclassification estimators as the number of subclasses goes to infinity. By formalizing this intuition, we propose novel propensity score weighting estimators that are both consistent and robust to model misspecification. Empirical studies show that the proposed estimators perform favorably compared to existing methods. In Chapter 3 we study identification and estimation of causal effects with outcomes truncated by death. It is common that in medical studies, the outcome of interest is truncated by death, meaning that a subject had died before the outcome could be measured. In this case, restricted analysis among survivors may be subject to selection bias. It is hence of interest to estimate the survivor average causal effect (SACE), defined as the average causal effect among subjects who would survive under either exposure. In this chapter, we consider the identification and estimation problems of the SACE. We propose to identify a substitution variable for the latent membership of the always-survivor group. The identifiability conditions required for a substitution variable are similar in idea to conditions for an instrumental variable. We show that the SACE is identifiable with use of a substitution variable, and propose novel model parameterizations for estimation of the SACE under our identification assumptions. Our approaches are illustrated via simulation studies and two data analyses. In Chapter 4, we study causal analysis of ordinal treatments and binary outcomes under truncation by death. It is common that in multi-arm randomized trials, the outcome of interest is “truncated by death,” meaning that it is only observed or well-defined conditioning on an intermediate outcome. In this case, in addition to pairwise contrasts, the joint inference for all treatment arms is also of interest. Under a monotonicity assumption we present methods for both pairwise and joint causal analyses of ordinal treatments and binary outcomes in presence of truncation by death. We illustrate via examples the appropriateness of our assumptions in different scientific contexts.

Download or read book The Economics of Artificial Intelligence written by Ajay Agrawal and published by University of Chicago Press. This book was released on 2024-03-05 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: A timely investigation of the potential economic effects, both realized and unrealized, of artificial intelligence within the United States healthcare system. In sweeping conversations about the impact of artificial intelligence on many sectors of the economy, healthcare has received relatively little attention. Yet it seems unlikely that an industry that represents nearly one-fifth of the economy could escape the efficiency and cost-driven disruptions of AI. The Economics of Artificial Intelligence: Health Care Challenges brings together contributions from health economists, physicians, philosophers, and scholars in law, public health, and machine learning to identify the primary barriers to entry of AI in the healthcare sector. Across original papers and in wide-ranging responses, the contributors analyze barriers of four types: incentives, management, data availability, and regulation. They also suggest that AI has the potential to improve outcomes and lower costs. Understanding both the benefits of and barriers to AI adoption is essential for designing policies that will affect the evolution of the healthcare system.

Download or read book Multiple Robust Estimation for the Average Treatment Effect with Combining External Auxiliary Information in the Presence of the Population Heterogeneity written by and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Causal Inference Beyond Estimating Average Treatment Effects written by Kwonsang Lee and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many scientific questions are to understand and reveal the causal mechanisms from observational study data or experimental data. Over the past several decades, there has been a large number of developments to render causal inferences from observed data. Most developments are designed to estimate the mean difference between treated and control groups that is often called the average treatment effect (ATE), and rely on identifying assumptions to allow causal interpretation. However, more specific treatment effects beyond the ATE can be estimated under the same assumptions. For example, instead of estimating the mean of potential outcomes in a group, we may want to estimate the distribution of the potential outcomes. Understanding the distribution implies understanding the mean, but not vice versa. Therefore, more sophisticated causal inference can be made from the data. The dissertation focuses on causal inference in observational studies, and discusses three main achievements. First, in instrumental variable (IV) models, we propose a novel nonparametric likelihood method for estimating the distributional treatment effect that compares two potential outcome distributions for treated and control groups. Furthermore, we provide a nonparametric likelihood ratio test for the hypothesis that the two potential outcome distributions are identical. Second, we develop two methods for discovering effect modification in a matched observational study data: (1) the CART method and (2) the Submax method. Both methods are applied to real data examples for finding effect modifiers that alter the magnitude of the treatment effect. Lastly, we provide a causal definition of the malaria attributable fever fraction (MAFF) that has not been studied in the causal inference field, and propose a novel maximum likelihood method to account for fever killing effect and measurement errors.