Download or read book Doubly Robust Estimation of Local Average Treatment Effects Using Inverse Probability Weighted Regression Adjustment written by Tymon Słoczyński and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We revisit the problem of estimating the local average treatment effect (LATE) and the local average treatment effect on the treated (LATT) when control variables are available, either to render the instrumental variable (IV) suitably exogenous or to improve precision. Unlike previous approaches, our doubly robust (DR) estimation procedures use quasi-likelihood methods weighted by the inverse of the IV propensity score - so-called inverse probability weighted regression adjustment (IPWRA) estimators. By properly choosing models for the propensity score and out-come models, fitted values are ensured to be in the logical range determined by the response variable, producing DR estimators of LATE and LATT with appealing small sample properties. Inference is relatively straightforward both analytically and using the nonparametric bootstrap. Our DR LATE and DR LATT estimators work well in simulations. We also propose a DR version of the Hausman test that can be used to assess the unconfoundedness assumption through a comparison of different estimates of the average treatment effect on the treated (ATT) under one-sided noncompliance. Unlike the usual test that compares OLS and IV estimates, this procedure is robust to treatment effect heterogeneity.

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 Performance of Augmented Inverse Probability Weighting Estimation for High dimensional Data written by Xiaoyu Wei and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "Doubly-robust estimators have been used extensively for estimating the treatment effect, for their property of being unbiased when either the outcome regression model or the propensity score model is correctly specified. As the number of data dimension increases nowadays, little is known about how these methods perform in high-dimensional data. In this thesis, we aimed to examine the performance of one doubly-robust estimator, augmented inverse probability weighting (AIPW) estimator, in such data. Several Monte Carlo simulation studies were conducted, and the treatment effect was estimated under both model specification and misspecification. Simulation results showed that propensity score estimation was challenging in such settings. Advanced methods other than multiple logistic regression should be utilized for propensity score estimation and eliminating imbalance. We also investigated further into a high-dimensional propensity score algorithm, a variable selection method for confounding adjustment in high-dimensional data. We incorporated this algorithm in the estimation process, and explored the optimal value for the number of variables to adjust for. Finally, we presented a plasmode simulation study based on a real data set from Clinical Practice Research Datalink, where the effect of post-myocardial infarction statin use on the rate of one-year mortality was studied." --

Download or read book Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score written by Keisuke Hirano and published by . This book was released on 2000 with total page 68 pages. Available in PDF, EPUB and Kindle. Book excerpt: We are interested in estimating the average effect of a binary treatment on a scalar outcome. If assignment to the treatment is independent of the potential outcomes given pretreatment variables, biases associated with simple treatment-control average comparisons can be removed by adjusting for differences in the pre-treatment variables. Rosenbaum and Rubin (1983, 1984) show that adjusting solely for differences between treated and control units in a scalar function of the pre-treatment, the propensity score, also removes the entire bias associated with differences in pre-treatment variables. Thus it is possible to obtain unbiased estimates of the treatment effect without conditioning on a possibly high-dimensional vector of pre-treatment variables. Although adjusting for the propensity score removes all the bias, this can come at the expense of efficiency. We show that weighting with the inverse of a nonparametric estimate of the propensity score, rather than the true propensity score, leads to efficient estimates of the various average treatment effects. This result holds whether the pre-treatment variables have discrete or continuous distributions. We provide intuition for this result in a number of ways. First we show that with discrete covariates, exact adjustment for the estimated propensity score is identical to adjustment for the pre-treatment variables. Second, we show that weighting by the inverse of the estimated propensity score can be interpreted as an empirical likelihood estimator that efficiently incorporates the information about the propensity score. Finally, we make a connection to results to other results on efficient estimation through weighting in the context of variable probability sampling.

Download or read book Marginal Treatment Effects in Difference in Differences written by Pedro Picchetti and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Difference-in-Differences (DiD) is a popular method used to evaluate the effect of a treatment. In its most simple version a control group remains untreated at two periods, whereas the treatment group becomes fully treated at the second period. However, it is not uncommon in applications of the method that the treatment rate only increases more in the treatment group. This article presents identification results for the marginal treatment effect (MTE) in such fuzzy designs. We show that we can modify the standard identifying assumptions in DiD designs with covariates to identify the MTE in models with essential heterogeneity. We propose two different procedures for the estimation of the MTE that rely on different assumptions regarding the potential outcomes model and prove their asymptotical normality. Furthermore, we derive a doubly-robust estimator for the local average treatment effect (LATE) which augments the two-way fixed effects regression model with a control function and unit-specific weights that rise from the propensity score. We assert the desirable finite-sample properties through simulation studies of a linear MTE model. Finally, we use our results to investigate heterogeneity on the returns to primary school attendance in Indonesia.

Download or read book Analysis of Observational Health Care Data Using SAS written by Douglas E. Faries and published by SAS Press. This book was released on 2010 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book guides researchers in performing and presenting high-quality analyses of all kinds of non-randomized studies, including analyses of observational studies, claims database analyses, assessment of registry data, survey data, pharmaco-economic data, and many more applications. The text is sufficiently detailed to provide not only general guidance, but to help the researcher through all of the standard issues that arise in such analyses. Just enough theory is included to allow the reader to understand the pros and cons of alternative approaches and when to use each method. The numerous contributors to this book illustrate, via real-world numerical examples and SAS code, appropriate implementations of alternative methods. The end result is that researchers will learn how to present high-quality and transparent analyses that will lead to fair and objective decisions from observational data. This book is part of the SAS Press program.

Download or read book Nonparametric IV Estimation of Local Average Treatment Effects with Covariates written by Markus Frölich and published by . This book was released on 2002 with total page 46 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Semiparametric Approaches for Average Causal Effect and Precision Medicine written by Trinetri Ghosh and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Average causal effect is often used to compare the treatments or interventions in both randomized and observational studies. It has a wide variety of applications in medical, natural, and social sciences, for example, psychology, political science, economics, and so on. Due to the increased availability of high-dimensional pre-treatment information sets, dimension reduction is a major methodological issue in observational studies to estimate the average causal effect of a non-randomized treatment. Often assumptions are made to ensure model identifiability and to establish theoretical guarantees for nuisance conditional models. But these assumptions can be less flexible. In the first work (Chapter 2), to estimate the average causal effect in an observational study, we use a semiparametric locally efficient dimension-reduction approach to assess the treatment assignment mechanisms and average responses in both the treated and the non-treated groups. We then integrate our results using imputation, inverse probability weighting, and doubly robust augmentation estimators. Doubly robust estimators are locally efficient, and imputation estimators are super-efficient when the response models are correct. To take advantage of both procedures, we introduce a shrinkage estimator that combines the two. The proposed estimators retain the double robustness property while improving on the variance when the response model is correct. We demonstrate the performance of these estimators using simulated experiments and a real data set on the effect of maternal smoking on baby birth weight. In the second work (Chapter 3), we implemented semiparametric efficient method in an emerging topic, precision medicine, an approach to tailoring disease prevention and treatment that takes into account individual variability in genes, environment, and lifestyle for each person. The goal of precision medicine is to deploy appropriate and optimal treatment based on the context of a patient's individual characteristics to maximize the clinical benefit. In this work, we propose a new modeling and estimation approach to select the optimal treatment regime from two different options through constructing a robust estimating equation. The method is protected against misspecification of the propensity score function or the outcome regression model for the non-treated group or the potential non-monotonic treatment difference model. Nonparametric smoothing and dimension reduction are incorporated to estimate the treatment difference model. We then identify the optimal treatment by maximizing the value function and established theoretical properties of the treatment assignment strategy. We illustrate the performance and effectiveness of our proposed estimators through extensive simulation studies and a real-world application to Huntington's disease patients. In the third work (Chapter 4), we aim to obtain optimal individualized treatment rules in the covariate-adjusted randomization clinical trial with many covariates. We model the treatment effect with an unspecified function of a single index of the covariates and leave the baseline response completely arbitrary. We devise a class of estimators to consistently estimate the treatment effect function and its associated index while bypassing the estimation of the baseline response, which is subject to the curse of dimensionality. We further develop inference tools to identify predictive covariates and isolate effective treatment regions. The usefulness of the methods is demonstrated in both simulations and a clinical data example.

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 Identification and Estimation of Local Average Treatment Effects written by Guido W. Imbens and published by . This book was released on 1995 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: We investigate conditions sufficient for identification of average treatment effects using instrumental variables. First we show that the existence of valid instruments is not sufficient to identify any meaningful average treatment effect. We then establish that the combination of an instrument and a condition on the relation between the instrument and the participation status is sufficient for identification of a local average treatment effect for those who can be induced to change their participation status by changing the value of the instrument. Finally we derive the probability limit of the standard IV estimator under these conditions. It is seen to be a weighted average of local average treatment effects.

Download or read book Semiparametric Theory and Missing Data written by Anastasios Tsiatis and published by Springer Science & Business Media. This book was released on 2007-01-15 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book summarizes current knowledge regarding the theory of estimation for semiparametric models with missing data, in an organized and comprehensive manner. It starts with the study of semiparametric methods when there are no missing data. The description of the theory of estimation for semiparametric models is both rigorous and intuitive, relying on geometric ideas to reinforce the intuition and understanding of the theory. These methods are then applied to problems with missing, censored, and coarsened data with the goal of deriving estimators that are as robust and efficient as possible.

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 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 Unified Methods for Censored Longitudinal Data and Causality written by Mark J. van der Laan and published by Springer Science & Business Media. This book was released on 2012-11-12 with total page 412 pages. Available in PDF, EPUB and Kindle. Book excerpt: A fundamental statistical framework for the analysis of complex longitudinal data is provided in this book. It provides the first comprehensive description of optimal estimation techniques based on time-dependent data structures. The techniques go beyond standard statistical approaches and can be used to teach masters and Ph.D. students. The text is ideally suitable for researchers in statistics with a strong interest in the analysis of complex longitudinal data.

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 Econometric Evaluation of Socio Economic Programs written by Giovanni Cerulli and published by Springer. This book was released on 2015-05-08 with total page 319 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides advanced theoretical and applied tools for the implementation of modern micro-econometric techniques in evidence-based program evaluation for the social sciences. The author presents a comprehensive toolbox for designing rigorous and effective ex-post program evaluation using the statistical software package Stata. For each method, a statistical presentation is developed, followed by a practical estimation of the treatment effects. By using both real and simulated data, readers will become familiar with evaluation techniques, such as regression-adjustment, matching, difference-in-differences, instrumental-variables and regression-discontinuity-design and are given practical guidelines for selecting and applying suitable methods for specific policy contexts.

Download or read book The Prevention and Treatment of Missing Data in Clinical Trials written by National Research Council and published by National Academies Press. This book was released on 2010-12-21 with total page 163 pages. Available in PDF, EPUB and Kindle. Book excerpt: Randomized clinical trials are the primary tool for evaluating new medical interventions. Randomization provides for a fair comparison between treatment and control groups, balancing out, on average, distributions of known and unknown factors among the participants. Unfortunately, these studies often lack a substantial percentage of data. This missing data reduces the benefit provided by the randomization and introduces potential biases in the comparison of the treatment groups. Missing data can arise for a variety of reasons, including the inability or unwillingness of participants to meet appointments for evaluation. And in some studies, some or all of data collection ceases when participants discontinue study treatment. Existing guidelines for the design and conduct of clinical trials, and the analysis of the resulting data, provide only limited advice on how to handle missing data. Thus, approaches to the analysis of data with an appreciable amount of missing values tend to be ad hoc and variable. The Prevention and Treatment of Missing Data in Clinical Trials concludes that a more principled approach to design and analysis in the presence of missing data is both needed and possible. Such an approach needs to focus on two critical elements: (1) careful design and conduct to limit the amount and impact of missing data and (2) analysis that makes full use of information on all randomized participants and is based on careful attention to the assumptions about the nature of the missing data underlying estimates of treatment effects. In addition to the highest priority recommendations, the book offers more detailed recommendations on the conduct of clinical trials and techniques for analysis of trial data.