Download or read book Trimmed Mean Group Estimation of Average Treatment Effects in Ultra Short T Panels Under Correlated Heterogeneity written by M. Hashem Pesaran and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Under correlated heterogeneity, the commonly used two-way fixed effects estimator is biased and can lead to misleading inference. This paper proposes a new trimmed mean group (TMG) estimator which is consistent at the irregular rate of n 1/3 even if the time dimension of the panel is as small as the number of its regressors. Extensions to panels with time effects are provided, and a Hausman-type test of correlated heterogeneity is proposed. Small sample properties of the TMG estimator (with and without time effects) are investigated by Monte Carlo experiments and shown to be satisfactory and perform better than other trimmed estimators proposed in the literature. The proposed test of correlated heterogeneity is also shown to have the correct size and satisfactory power. The utility of the TMG approach is illustrated with an empirical application.

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 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 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 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 Statistical Methods to Study Heterogeneity of Treatment Effects written by Lin H. Taft and published by . This book was released on 2016 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: Randomized studies are designed to estimate the average treatment effect (ATE) of an intervention. Individuals may derive quantitatively, or even qualitatively, different effects from the ATE, which is called the heterogeneity of treatment effect. It is important to detect the existence of heterogeneity in the treatment responses, and identify the different sub-populations. Two corresponding statistical methods will be discussed in this talk: a hypothesis testing procedure and a mixture-model based approach. The hypothesis testing procedure was constructed to test for the existence of a treatment effect in sub-populations. The test is nonparametric, and can be applied to all types of outcome measures. A key innovation of this test is to build stochastic search into the test statistic to detect signals that may not be linearly related to the multiple covariates. Simulations were performed to compare the proposed test with existing methods. Power calculation strategy was also developed for the proposed test at the design stage. The mixture-model based approach was developed to identify and study the sub-populations with different treatment effects from an intervention. A latent binary variable was used to indicate whether or not a subject was in a sub-population with average treatment benefit. The mixture-model combines a logistic formulation of the latent variable with proportional hazards models. The parameters in the mixture-model were estimated by the EM algorithm. The properties of the estimators were then studied by the simulations. Finally, all above methods were applied to a real randomized study in a low ejection fraction population that compared the Implantable Cardioverter Defibrillator (ICD) with conventional medical therapy in reducing total mortality.

Download or read book Semiparametric Estimation of Treatment Effects in Randomized Experiments written by Susan Athey and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: We develop new semiparametric methods for estimating treatment effects. We focus on a setting where the outcome distributions may be thick tailed, where treatment effects are small, where sample sizes are large and where assignment is completely random. This setting is of particular interest in recent experimentation in tech companies. We propose using parametric models for the treatment effects, as opposed to parametric models for the full outcome distributions. This leads to semiparametric models for the outcome distributions. We derive the semiparametric efficiency bound for this setting, and propose efficient estimators. In the case with a constant treatment effect one of the proposed estimators has an interesting interpretation as a weighted average of quantile treatment effects, with the weights proportional to (minus) the second derivative of the log of the density of the potential outcomes. Our analysis also results in an extension of Huber's model and trimmed mean to include asymmetry and a simplified condition on linear combinations of order statistics, which may be of independent interest.

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 Can Variation in Subgroups s Average Treatment Effects Explain Treatment Effect Heterogeneity written by Marianne P. Bitler and published by . This book was released on 2014 with total page 35 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 Treatment Heterogeneity and Potential Outcomes in Linear Mixed Effects Models written by Troy E. Richardson and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Studies commonly focus on estimating a mean treatment effect in a population. However, in some applications the variability of treatment effects across individual units may help to characterize the overall effect of a treatment across the population. Consider a set of treatments, {T, C}, where T denotes some treatment that might be applied to an experimental unit and C denotes a control. For each of N experimental units, the duplet {gamma[subscript]Ti, gamma[subscript]Ci}, i=1,2 ..., N, represents the potential response of the i[superscript]th experimental unit if treatment were applied and the response of the experimental unit if control were applied, respectively. The causal effect of T compared to C is the difference between the two potential responses, gamma[subscript]Ti- gamma[subscript]Ci. Much work has been done to elucidate the statistical properties of a causal effect, given a set of particular assumptions. Gadbury and others have reported on this for some simple designs and primarily focused on finite population randomization based inference. When designs become more complicated, the randomization based approach becomes increasingly difficult. Since linear mixed effects models are particularly useful for modeling data from complex designs, their role in modeling treatment heterogeneity is investigated. It is shown that an individual treatment effect can be conceptualized as a linear combination of fixed treatment effects and random effects. The random effects are assumed to have variance components specified in a mixed effects "potential outcomes" model when both potential outcomes, gamma[subscript]T, gamma[subscript]C, are variables in the model. The variance of the individual causal effect is used to quantify treatment heterogeneity. Post treatment assignment, however, only one of the two potential outcomes is observable for a unit. It is then shown that the variance component for treatment heterogeneity becomes non-estimable in an analysis of observed data. Furthermore, estimable variance components in the observed data model are demonstrated to arise from linear combinations of the non-estimable variance components in the potential outcomes model. Mixed effects models are considered in context of a particular design in an effort to illuminate the loss of information incurred when moving from a potential outcomes framework to an observed data analysis.

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 Examining the Foundations of Methods That Assess Treatment Effect Heterogeneity Across Intermediate Outcomes written by Avi Feller and published by . This book was released on 2015 with total page 7 pages. Available in PDF, EPUB and Kindle. Book excerpt: The goal of this study is to better understand how methods for estimating treatment effects of latent groups operate. In particular, the authors identify where violations of assumptions can lead to biased estimates, and explore how covariates can be critical in the estimation process. For each set of approaches, the authors first review the assumptions necessary for identification and discuss practical issues that arise in estimation; second, they then examine how covariates allow for improved estimation, and determine the conditions necessary for using covariates to identify causal effects in latent groups; and third, they then compare the different methods using simulation studies built from datasets constructed by imputing missing class membership and potential outcomes from real-world studies. This allows for examining the performance of the different techniques under a variety of plausible circumstances. Analyzed is data from the Job Search Intervention Study (JOBS II), a randomized evaluation of an intervention for unemployed workers consisting of a series of training sessions and also the Head Start Impact Study, a large-scale randomized evaluation of the Head Start program in which children randomized to treatment were offered a seat in a classroom in a Head Start program. The authors conclude that, in practice, randomized trials should attempt to collect such covariates by, for example, having expert assessment of likelihood of compliance collected at baseline and that for identification, many methods require assumptions that are quite strong.

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 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 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 130 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 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.