Download or read book Estimation of Average Treatment Effects with Misclassification written by and published by . This book was released on 2003 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Inference on Local Average Treatment Effects for Misclassified Treatment written by Takahide Yanagi and published by . This book was released on 2018 with total page 51 pages. Available in PDF, EPUB and Kindle. Book excerpt: We develop point-identification for the local average treatment effect when the binary treatment contains a measurement error. The standard instrumental variable estimator is inconsistent for the parameter since the measurement error is non-classical by construction. We correct the problem by identifying the distribution of the measurement error based on the use of an exogenous variable that can even be a binary covariate. The moment conditions derived from the identification lead to generalized method of moments estimation with asymptotically valid inferences. Monte Carlo simulations and an empirical illustration demonstrate the usefulness of the proposed procedure.

Download or read book Semiparametric Single index Estimation for Average Treatment Effects written by Difang Huang and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 Statistical Analysis with Measurement Error or Misclassification written by Grace Y. Yi and published by Springer. This book was released on 2017-08-02 with total page 497 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph on measurement error and misclassification covers a broad range of problems and emphasizes unique features in modeling and analyzing problems arising from medical research and epidemiological studies. Many measurement error and misclassification problems have been addressed in various fields over the years as well as with a wide spectrum of data, including event history data (such as survival data and recurrent event data), correlated data (such as longitudinal data and clustered data), multi-state event data, and data arising from case-control studies. Statistical Analysis with Measurement Error or Misclassification: Strategy, Method and Application brings together assorted methods in a single text and provides an update of recent developments for a variety of settings. Measurement error effects and strategies of handling mismeasurement for different models are closely examined in combination with applications to specific problems. Readers with diverse backgrounds and objectives can utilize this text. Familiarity with inference methods—such as likelihood and estimating function theory—or modeling schemes in varying settings—such as survival analysis and longitudinal data analysis—can result in a full appreciation of the material, but it is not essential since each chapter provides basic inference frameworks and background information on an individual topic to ease the access of the material. The text is presented in a coherent and self-contained manner and highlights the essence of commonly used modeling and inference methods. This text can serve as a reference book for researchers interested in statistical methodology for handling data with measurement error or misclassification; as a textbook for graduate students, especially for those majoring in statistics and biostatistics; or as a book for applied statisticians whose interest focuses on analysis of error-contaminated data. Grace Y. Yi is Professor of Statistics and University Research Chair at the University of Waterloo. She is the 2010 winner of the CRM-SSC Prize, an honor awarded in recognition of a statistical scientist's professional accomplishments in research during the first 15 years after having received a doctorate. She is a Fellow of the American Statistical Association and an Elected Member of the International Statistical Institute.

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 Mismeasurement and efficiency estimates Evidence from smallholder survey data in Africa written by Abay, Kibrom A. and published by Intl Food Policy Res Inst. This book was released on 2022-02-09 with total page 43 pages. Available in PDF, EPUB and Kindle. Book excerpt: Smallholder agriculture in sub-Saharan Africa is commonly characterized by high levels of technical inefficiency. However, much of this characterization relies on self-reported input and production data, which are prone to systematic measurement error. We theoretically show that non-classical measurement error introduces multiple identification challenges and sources of bias in estimating smallholders’ technical inefficiency. We then empirically examine the implications of measurement error for the estimation of technical inefficiency using smallholder farm survey data from Ethiopia, Malawi, Nigeria, and Tanzania. We find that measurement error in agricultural input and production data leads to a substantial upward bias in technical inefficiency estimates (by up to 85 percent for some farmers). Our results suggest that existing estimates of technical efficiency in sub-Saharan Africa may be severe underestimates of smallholders’ actual efficiency and what is commonly attributed to farmer inefficiency may be an artifact of mismeasurement in agricultural data. Our results raise questions about the received wisdom on African smallholders’ production efficiency and prior estimates of the productivity of agricultural inputs. Improving the measurement of agricultural data can improve our understanding of smallholders’ production efficiencies and improve the targeting of productivity-enhancing technologies.

Download or read book Estimation of Average Treatment Effects Using Panel Data when Treatment Effect Heterogeneity Depends on Unobserved Fixed Effects written by Shosei Sakaguchi and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper proposes a new panel data approach to identify and estimate the time-varying average treatment effect (ATE). The approach allows for treatment effect heterogeneity that depends on unobserved fixed effects. In the presence of this type of heterogeneity, existing panel data approaches identify the ATE for limited subpopulations only. In contrast, the proposed approach identifies and estimates the ATE for the entire population. The approach relies on the linear fixed effects specification of potential outcome equations and uses exogenous variables that are correlated with the fixed effects. I apply the approach to study the impact of a mother's smoking during pregnancy on her child's birth weight.

Download or read book Risk Assessment and Evaluation of Predictions written by Mei-Ling Ting Lee and published by Springer Science & Business Media. This book was released on 2013-12-11 with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt: Methods of risk analysis and the outcome of particular evaluations and predictions are covered in detail in this proceedings volume, whose contributions are based on invited presentations from Professor Mei-Ling Ting Lee's 2011 symposium on Risk Analysis and the Evaluation of Predictions. This symposium was held at the University of Maryland in October of 2011. Risk analysis is the science of evaluating health, environmental, and engineering risks resulting from past, current, or anticipated, future activities. The use of these evaluations include to provide information for determining regulatory actions to limit risk, present scientific evidence in legal settings, evaluate products and potential liabilities within private organizations, resolve World Trade disputes amongst nations, and educate the public concerning particular risk issues. Risk analysis is an interdisciplinary science that relies on epidemiology and laboratory studies, collection of exposure and other field data, computer modeling, and related social, economic and communication considerations. In addition, social dimensions of risk are addressed by social scientists.

Download or read book Moving the Goalposts written by and published by . This book was released on 2006 with total page 46 pages. Available in PDF, EPUB and Kindle. Book excerpt: Estimation of average treatment effects under unconfoundedness or exogenous treatment assignment is often hampered by lack of overlap in the covariate distributions. This lack of overlap can lead to imprecise estimates and can make commonly used estimators sensitive to the choice of specification. In such cases researchers have often used informal methods for trimming the sample. In this paper we develop a systematic approach to addressing such lack of overlap. We characterize optimal subsamples for which the average treatment effect can be estimated most precisely, as well as optimally weighted average treatment effects. Under some conditions the optimal selection rules depend solely on the propensity score. For a wide range of distributions a good approximation to the optimal rule is provided by the simple selection rule to drop all units with estimated propensity scores outside the range [0.1,0.9]

Download or read book Higher order Optimal Estimation of Binary Average Treatment Effects written by Paul Joseph Gift and published by . This book was released on 2002 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Three Essays on the Estimation of Average Treatment Effects in Quasi Experimental Panel Data written by Kathleen T. Li and published by . This book was released on 2018 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: Identifying average treatment effects (ATE) from quasi-experimental panel data has become one of the most important yet challenging endeavors for social scientists. The difficulty lies in accurately estimating the counterfactual outcomes for the potentially treated units in the absence of treatment. Perhaps the most popular method to estimate average treatment effects is the Difference-in-Differences (DID) method. The key assumption of the DID method is that outcomes of the treated units would have followed a path parallel to the control units in the absence of treatment and violation of this ``parallel lines" assumption will result in biased estimates. This dissertation consists of three essays, which either build on existing methods (essay 1 and 3) or propose a new method (essay 2) that can be used even when the ``parallel lines" assumption of DID does not hold. In essay 1, we derive the asymptotic distribution of the HCW method, which is computationally simple as it only involves least squares regressions. However, in cases where treatment and control units are positively correlated, the HCW method may have less predictive efficiency than other methods such as the synthetic control and modified synthetic control method, which impose the restriction that weights are non-negative. The popular synthetic control method additionally imposes the restriction that the weights sum to one, which can be a helpful regularization condition when there are many control units. In essay 3, we provide the inference theory for both the synthetic control and modified synthetic control method through projection theory and propose a computational algorithm using subsampling to compute the confidence intervals. In order to apply the HCW method, synthetic control method and modified synthetic control method, the number of control units needs to be smaller than the pre-treatment sample size. In essay 2, we propose the augmented DID method, which can be used where there are many treatment and control units, but is less flexible than the three aforementioned methods. In short, this dissertation provides several methods and their inference procedures to identify average treatment effects. Which method should be used when depends on the structure of the data.

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 Missing Data Methods written by David M. Drukker and published by Emerald Group Publishing. This book was released on 2011-11-23 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: Contains 16 chapters authored by specialists in the field, covering topics such as: Missing-Data Imputation in Nonstationary Panel Data Models; Markov Switching Models in Empirical Finance; Bayesian Analysis of Multivariate Sample Selection Models Using Gaussian Copulas; and, Consistent Estimation and Orthogonality.

Download or read book Estimating the Average Treatment Effect Using the Cluster Hierarchy and Merge Post stratification Method written by Kingsley Darko and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Randomized experiments help reduce bias in estimates of the average treatment effect by ensuring that confounders have the same distribution across treatment groups. However, some randomizations can still have imbalances on important confounders, which can lead to inaccurate estimates. Post-stratification is one method for correcting these imbalances to improve estimates. In post-stratification, we form groups of units, called strata, and estimate the overall treatment effect by taking a weighted average of treatment effects within each stratum. In practice, strata are formed based on the values of the confounders. We examine the ad-hoc post-stratification method, where we form groups of units so that every group has at least one treated and control unit. A sufficient condition for the unbiasedness of post-stratification estimators is treatment assignment symmetry-that conditioned on the number of treated units within each stratum, each treatment assignment is equally likely. However, ensuring that each stratum has at least one treatment status often violates assignment symmetry and leads to biased estimates. This report considers a new method for forming strata- cluster hierarchy and merge post-stratification (CHAMP)-that ensures that each treatment status is represented within each stratum and satisfies a weaker form of assignment symmetry required for unbiased estimation. We perform a simulation study to compare CHAMP post-stratification with ad-hoc methods for forming strata. We show that CHAMP post-stratification successfully eliminates bias while ensuring small standard errors of post-stratification estimators. Finally, we apply our method to the Study to Understand Prognoses and Preferences for Outcomes and Risks and Treatments (SUPPORT) dataset to assess the efficacy of right heart catheterization in the initial care of critically ill patients.

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 Econometrics in a Formal Science of Economics written by Bernt P. Stigum and published by MIT Press. This book was released on 2015 with total page 389 pages. Available in PDF, EPUB and Kindle. Book excerpt: An examination of the role of theory in applied econometrics.