Download or read book Identification of Population Average Treatment Effects Using Nonlinear Instrumental Variables Estimators written by Cole Garrett Chapman and published by . This book was released on 2014 with total page 153 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nonlinear two-stage residual inclusion (2SRI) estimators have become increasingly favored over traditional linear two-stage least squares (2SLS) methods for instrumental variables analysis of empirical models with inherently nonlinear dependent variables. Rising adoption of nonlinear 2SRI is largely attributable to simulation evidence showing that nonlinear 2SRI generates consistent estimates of population average treatment effects in nonlinear models, while 2SLS and nonlinear 2SPS do not. However, while it is believed that consistency of 2SRI for population average treatment effects is a general result, current evidence is limited to simulations performed under unique and restrictive settings with regards to treatment effect heterogeneity and conditions underlying treatment choices. This research contributes by describing existing simulation evidence and investigating the ability to generate absolute estimates of population average treatment effects (ATE) and local average treatment effects (LATE) using common IV estimators using Monte Carlo simulation methods across 10 alternative scenarios of treatment effect heterogeneity and sorting-on-the-gain. Additionally, estimates for the effect of ACE/ARBs on 1-year survival for Medicare beneficiaries with acute myocardial infarction are generated and compared across alternative linear and nonlinear IV estimators. Simulation results show that, while 2SLS generates unbiased and consistent estimates of LATE across all scenarios, nonlinear 2SRI generates unbiased estimates of ATE only under very restrictive settings. If marginal patients are unique in terms of treatment effectiveness, then nonlinear 2SRI cannot be expected to generate unbiased or consistent estimates of ATE unless all factors related to treatment effect heterogeneity are fully measured.

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 Instrumental Variables Estimation of Average Treatment Effects in Econometrics and Epidemiology written by Joshua David Angrist and published by . This book was released on 1991 with total page 68 pages. Available in PDF, EPUB and Kindle. Book excerpt: The average effect of intervention or treatment is a parameter of interest in both epidemiology and econometrics. A key difference between applications in the two fields is that epidemiologic research is more likely to involve qualitative outcomes and nonlinear models. An example is the recent use of the Vietnam era draft lottery to construct estimates of the effect of Vietnam era military service on civilian mortality. In this paper. I present necessary and sufficient conditions for linear instrumental variables. techniques to consistently estimate average treatment effects in qualitative or other nonlinear models. Most latent index models commonly applied to qualitative outcomes in econometrics fail to satisfy these conditions, and monte carlo evidence on the bias of instrumental estimates of the average treatment effect in a bivariate probit model is presented. The evidence suggests that linear instrumental variables estimators perform nearly as well as the correctly specified maximum likelihood estimator. especially in large samples. Linear instrumental variables and the normal maximum likelihood estimator are also remarkably robust to non-normality.

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 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 Aspects of Identification and Partial Identification of Average Treatment Effect in Binary Outcome Models written by Chuhui Li and published by . This book was released on 2015 with total page 293 pages. Available in PDF, EPUB and Kindle. Book excerpt: Average treatment effect (ATE) is a measure that is frequently used in empirical analysis for measuring the impact of a policy amendable treatment on an outcome variable. Identification and estimation of the ATE have been of concern in empirical studies, as individuals are often only observed for one of the two treatment states in non-experimental data and the selection of treatment is often endogenous. This thesis studies the identification and estimation of the ATE of a binary treatment variable on a binary outcome variable. It particularly focuses on the implication of recent theoretical developments in the literature of partial identification to the econometric estimation of policy relevant effects in empirical applications.The notion of partial identification relates to the idea that in certain situations such as limited observability, more than one data generating process (DGP), or model, can give rise to the same data set we observe; these models are said to be observationally equivalent. In such circumstances policy relevant measures such as the ATE can not be point identified. It is only possible to set identify the measure by estimating an identified set (or bound) for such measures where all values in the set are consistent with the data.The analysis in the thesis is divided to three parts. The first part assumes that data is generated from a particular DGP with an additive error and a parametric distribution. It is found that the bias in the ATE estimator arising from a mis-specified error distribution is not significantly large if we have reasonable sample size and IV strength, even though there may be more significant biases for the model coefficients estimators. We also show that under this regime, the ATE can still be estimated reasonably well even without the existence of instrumental variables (IVs), relying on the assumed functional form and sample size for identification. The main part of the analysis is carried out in the remaining chapters under the partial identification framework. Performances of the estimated ATE bounds from four different estimation methods are compared by using the Hausdorff distance and Euclidean distance. It is found that for all sample sizes in the simulation, the easy to implement parametric methods yield better estimates than nonparametric methods. The strength of IVs also plays an important role on the partial identification of the ATE. The width of the identified set drops as the instrument strength grows. If an extremely strong instrumental variable is available, we may be able to achieve point identification of the ATE (the upper bound and lower bound will overlap). The simulation results further confirms that estimators from parametric methods are robust with regard to instrument strength, while the nonparametric estimators will deviate significantly from the true when the instrument strength is relatively small. Finally the point identification and partial identification of the ATE are applied to a real world data set to study the impact of the private health insurance status on dental service utilisation in Australia.The analysis in the thesis shows that conventional empirical analysis assuming a bivariate probit model could be misleading by estimating a much smaller range for the policy effect. This thesis illustrates with practical applications how various bound analysis of the ATE can be carried out and can provide more robust estimates for policy effects under much broader assumptions for the DGP.

Download or read book Endogeneity and Non response Bias in Treatment Evaluation Nonparametric Identification of Causal Effects by Instruments written by and published by . This book was released on 2015 with total page 33 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 The Estimation of Causal Effects by Difference in difference Methods written by Michael Lechner and published by Foundations and Trends(r) in E. This book was released on 2011 with total page 72 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph presents a brief overview of the literature on the difference-in-difference estimation strategy and discusses major issues mainly using a treatment effect perspective that allows more general considerations than the classical regression formulation that still dominates the applied work.

Download or read book Using Multisite Instrumental Variables to Estimate Treatment Effects and Treatment Effect Heterogeneity written by Christopher Ryan Runyon and published by . This book was released on 2020 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multisite randomized trials (MSTs) are an attractive research design to test the efficacy of an educational program at scale. Population models examining data from MSTs can provide information on the range of possible treatment effects that sites (such as schools) can expect from an educational program, even for those sites not included in the study. However, when some individuals at a site do not comply with their treatment assignment, conventional multilevel and meta-analytic estimation methods do not provide information on the effect of actually participating in the educational program. Instrumental variables (IV) is a method that can produce consistent estimates of the causal effect of participating in an educational program for those individuals that comply with their treatment assignment, an estimand called the complier-average treatment effect (CATE). IV methods for single-site trials are well understood and widely-used. Recently multisite IV models have been proposed to estimate the CATE and CATE heterogeneity across a population of sites, but the performance of these estimators has not been examined in a simulation study. Using Monte Carlo simulation, the current study examines the performance of three IV estimators and two conventional estimators in recovering the CATE and CATE heterogeneity under simulation conditions that resemble multisite trials of well-known educational programs

Download or read book Simple and Bias corrected Matching Estimators for Average Treatment Effects written by Alberto Abadie and published by . This book was released on 2002 with total page 72 pages. Available in PDF, EPUB and Kindle. Book excerpt: Matching estimators for average treatment effects are widely used in evaluation research despite the fact that their large sample properties have not been established in many cases. In this article, we develop a new framework to analyze the properties of matching estimators and establish a number of new results. First, we show that matching estimators include a conditional bias term which may not vanish at a rate faster than root-N when more than one continuous variable is used for matching. As a result, matching estimators may not be root-N-consistent. Second, we show that even after removing the conditional bias, matching estimators with a fixed number of matches do not reach the semiparametric efficiency bound for average treatment effects, although the efficiency loss may be small. Third, we propose a bias-correction that removes the conditional bias asymptotically, making matching estimators root-N-consistent. Fourth, we provide a new estimator for the conditional variance that does not require consistent nonparametric estimation of unknown functions. We apply the bias-corrected matching estimators to the study of the effects of a labor market program previously analyzed by Lalonde (1986). We also carry out a small simulation study based on Lalonde's example where a simple implementation of the biascorrected matching estimator performs well compared to both simple matching estimators and to regression estimators in terms of bias and root-mean-squared-error. Software for implementing the proposed estimators in STATA and Matlab is available from the authors on the web.

Download or read book Handbook of Quantile Regression written by Roger Koenker and published by CRC Press. This book was released on 2017-10-12 with total page 739 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quantile regression constitutes an ensemble of statistical techniques intended to estimate and draw inferences about conditional quantile functions. Median regression, as introduced in the 18th century by Boscovich and Laplace, is a special case. In contrast to conventional mean regression that minimizes sums of squared residuals, median regression minimizes sums of absolute residuals; quantile regression simply replaces symmetric absolute loss by asymmetric linear loss. Since its introduction in the 1970's by Koenker and Bassett, quantile regression has been gradually extended to a wide variety of data analytic settings including time series, survival analysis, and longitudinal data. By focusing attention on local slices of the conditional distribution of response variables it is capable of providing a more complete, more nuanced view of heterogeneous covariate effects. Applications of quantile regression can now be found throughout the sciences, including astrophysics, chemistry, ecology, economics, finance, genomics, medicine, and meteorology. Software for quantile regression is now widely available in all the major statistical computing environments. The objective of this volume is to provide a comprehensive review of recent developments of quantile regression methodology illustrating its applicability in a wide range of scientific settings. The intended audience of the volume is researchers and graduate students across a diverse set of disciplines.

Download or read book The Handbook of Historical Economics written by Alberto Bisin and published by Elsevier. This book was released on 2021-04-27 with total page 1002 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Handbook of Historical Economics guides students and researchers through a quantitative economic history that uses fully up-to-date econometric methods. The book's coverage of statistics applied to the social sciences makes it invaluable to a broad readership. As new sources and applications of data in every economic field are enabling economists to ask and answer new fundamental questions, this book presents an up-to-date reference on the topics at hand. Provides an historical outline of the two cliometric revolutions, highlighting the similarities and the differences between the two Surveys the issues and principal results of the "second cliometric revolution" Explores innovations in formulating hypotheses and statistical testing, relating them to wider trends in data-driven, empirical economics

Download or read book Statistical Methods for Studying Heterogeneous Treatment Effects with Instrumental Variables written by Michael William Johnson and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: There is a growing interest in estimating heterogeneous treatment effects in randomized and observational studies. However, most of the work relies on the assumption of ignorability, or no unmeasured confounding on the treatment effect. While instrumental variables (IV) are a popular technique to control for unmeasured confounding, there has been little research conducted to study heterogeneous treatment effects with the use of an IV. This dissertation introduces methods using an IV to discover novel subgroups, estimate their heterogeneous treatment effects, and identify individualized treatment rules (ITR) when ignorability is expected to be violated. In Chapter 2, we present a two-part algorithm to estimate heterogeneous treatment effects and detect novel subgroups using an IV with matching. The first part uses interpretable machine learning techniques, such as classification and regression trees, to discover potential effect modifiers. The second part uses closed testing to test for statistical significance of each effect modifier while strongly controlling the familywise error rate. We apply this method on the Oregon Health Insurance Experiment, estimating the effect of Medicaid on the number of days an individual's health does not impede their usual activities by using a randomized lottery as an instrument. In Chapter 3, we generalize methods to identify ITR using a binary IV to using multiple, discrete valued instruments, or equivalently, multilevel instruments. Several new problems arise when generalizing to multilevel instruments, requiring novel solutions. In particular, multilevel IV give rise to many latent subgroups that may experience heterogeneous treatment effects. Additionally, it may be unclear how to combine and compare the different levels of the IV to estimate treatment heterogeneity. We provide methods that use a prediction of the latent subgroup to identify optimal ITR, and methods to dynamically combine levels of the multilevel IV to estimate the heterogeneous treatment effects, effectively individualizing estimation of an ITR. Further, we provide and discuss necessary and sufficient conditions to identify an optimal ITR using a multilevel IV. We apply our methods to identify an ITR for two competing treatments, carotid endarterectomy and carotid artery stenting, on preventing stroke or death within 30 days of their index procedure.

Download or read book Estimating Person centered Treatment PET Effects Using Instrumental Variables written by Anirban Basu (Professor of health economics) and published by . This book was released on 2012 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper builds on the methods of local instrumental variables developed by Heckman and Vytlacil (1999, 2001, 2005) to estimate person-centered treatment (PeT) effects that are conditioned on the person's observed characteristics and averaged over the potential conditional distribution of unobserved characteristics that lead them to their observed treatment choices. PeT effects are more individualized than conditional treatment effects from a randomized setting with the same observed characteristics. PeT effects can be easily aggregated to construct any of the mean treatment effect parameters and, more importantly, are well-suited to comprehend individual-level treatment effect heterogeneity. The paper presents the theory behind PeT effects, studies their finite-sample properties using simulations and presents a novel analysis of treatment evaluation in health care.

Download or read book Estimating Person centered Treatment PeT Effects Using Instrumental Variables written by Anirban Basu (Professor of health economics) and published by . This book was released on 2012 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper builds on the methods of local instrumental variables developed by Heckman and Vytlacil (1999, 2001, 2005) to estimate person-centered treatment (PeT) effects that are conditioned on the person's observed characteristics and averaged over the potential conditional distribution of unobserved characteristics that lead them to their observed treatment choices. PeT effects are more individualized than conditional treatment effects from a randomized setting with the same observed characteristics. PeT effects can be easily aggregated to construct any of the mean treatment effect parameters and, more importantly, are well-suited to comprehend individual-level treatment effect heterogeneity. The paper presents the theory behind PeT effects, studies their finite-sample properties using simulations and presents a novel analysis of treatment evaluation in health care.

Download or read book Identification and Inference for Econometric Models written by Donald W. K. Andrews and published by Cambridge University Press. This book was released on 2005-06-17 with total page 606 pages. Available in PDF, EPUB and Kindle. Book excerpt: This 2005 collection pushed forward the research frontier in four areas of theoretical econometrics.