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Author: Haitian Xie
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Languages : en
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This dissertation is a collection of three essays on the econometric analysis of causal inference methods. Chapter 1 examines the identification and estimation of the structural function in fuzzy RD designs with a continuous treatment variable. We show that the nonlinear and nonseparable structural function can be nonparametrically identified at the RD cutoff under shape restrictions, including monotonicity and smoothness conditions. Based on the nonparametric identification equation, we propose a three-step semiparametric estimation procedure and establish the asymptotic normality of the estimator. The semiparametric estimator achieves the same convergence rate as in the case of a binary treatment variable. As an application of the method, we estimate the causal effect of sleep time on health status by using the discontinuity in natural light timing at time zone boundaries. Chapter 2 examines the local linear regression (LLR) estimate of the conditional distribution function F(y|x). We derive three uniform convergence results: the uniform bias expansion, the uniform convergence rate, and the uniform asymptotic linear representation. The uniformity in the above results is with respect to both x and y and therefore has not previously been addressed in the literature on local polynomial regression. Such uniform convergence results are especially useful when the conditional distribution estimator is the first stage of a semiparametric estimator. Chapter 3 studies the estimation of causal parameters in the generalized local average treatment effect model, a generalization of the classical LATE model encompassing multi-valued treatment and instrument. We derive the efficient influence function (EIF) and the semiparametric efficiency bound for two types of parameters: local average structural function (LASF) and local average structural function for the treated (LASF-T). The moment condition generated by the EIF satisfies two robustness properties: double robustness and Neyman orthogonality. Based on the robust moment condition, we propose the double/debiased machine learning (DML) estimators for LASF and LASF-T. We also propose null-restricted inference methods that are robust against weak identification issues. As an empirical application, we study the effects across different sources of health insurance by applying the developed methods to the Oregon Health Insurance Experiment.
Author: Haitian Xie
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Languages : en
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This dissertation is a collection of three essays on the econometric analysis of causal inference methods. Chapter 1 examines the identification and estimation of the structural function in fuzzy RD designs with a continuous treatment variable. We show that the nonlinear and nonseparable structural function can be nonparametrically identified at the RD cutoff under shape restrictions, including monotonicity and smoothness conditions. Based on the nonparametric identification equation, we propose a three-step semiparametric estimation procedure and establish the asymptotic normality of the estimator. The semiparametric estimator achieves the same convergence rate as in the case of a binary treatment variable. As an application of the method, we estimate the causal effect of sleep time on health status by using the discontinuity in natural light timing at time zone boundaries. Chapter 2 examines the local linear regression (LLR) estimate of the conditional distribution function F(y|x). We derive three uniform convergence results: the uniform bias expansion, the uniform convergence rate, and the uniform asymptotic linear representation. The uniformity in the above results is with respect to both x and y and therefore has not previously been addressed in the literature on local polynomial regression. Such uniform convergence results are especially useful when the conditional distribution estimator is the first stage of a semiparametric estimator. Chapter 3 studies the estimation of causal parameters in the generalized local average treatment effect model, a generalization of the classical LATE model encompassing multi-valued treatment and instrument. We derive the efficient influence function (EIF) and the semiparametric efficiency bound for two types of parameters: local average structural function (LASF) and local average structural function for the treated (LASF-T). The moment condition generated by the EIF satisfies two robustness properties: double robustness and Neyman orthogonality. Based on the robust moment condition, we propose the double/debiased machine learning (DML) estimators for LASF and LASF-T. We also propose null-restricted inference methods that are robust against weak identification issues. As an empirical application, we study the effects across different sources of health insurance by applying the developed methods to the Oregon Health Insurance Experiment.
Author: Hugo Bodory
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Languages : en
Pages : 0
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This doctoral thesis consists of four chapters. Each of the studies builds on the concept of causal inference. Two papers are empirical applications that analyze the effects of welfare dependency on health and health-related behavior. The remaining papers are methodological contributions to the literature on treatment effects, which focus on the introduction and evaluation of inference methods. The first chapter investigates whether welfare dependency has an impact on individual health and health-related behavior. The empirical analysis uses panel survey data to study health-related effects of the major German welfare program Hartz IV. Using a sample of individuals initially on welfare, the paper compares the health outcomes of two groups: those who remain on welfare and those who get off welfare. The findings show that welfare dependency can be detrimental to the health of individuals, as well as to their sports-related behavior. The second chapter conducts a mediation analysis to identify potential channels that can influence the health conditions of welfare recipients. The study uses a semi-parametric estimation method especially adapted to this mediation analysis to compute the effects on health. Evidence suggests that employment enhances the health of males and older individuals when getting off welfare. In contrast, health improvements for females cannot be attributed to employment but to the direct (or residual) effect of leaving welfare. The health of younger individuals is not affected by welfare dependency. The third chapter investigates the finite sample properties of a range of inference methods for treatment effect estimators. The simulations, based on empirical data, use both asymptotic approximations of analytical variances and bootstrap methods to compute confidence intervals and p-values. The results suggest that, in general, the bootstrap approaches outperform the analytical variance approximations in terms of s.
Author: Dmitry Arkhangelskiy
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Languages : en
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In this dissertation, I propose novel approaches to causal inference in the settings characterized by an explicit clustering structure. I study different aspects of this problem, considering settings with few large clusters as well as with many small clusters. The dissertation consists of two essays. The first essay proposes a new model for causal inference in the settings with few large clusters and cluster-level treatment assignment. The second essay studies causal inference questions in the settings with many clusters of moderate size and individual-level treatment assignment. In the first essay, I construct a nonlinear model for causal inference in the empirical settings where researchers observe individual-level data for few large clusters over at least two time periods. It allows for identification (sometimes partial) of the counterfactual distribution, in particular, identifying average treatment effects and quantile treatment effects. The model is flexible enough to handle multiple outcome variables, multidimensional heterogeneity, and multiple clusters. It applies to the settings where the new policy is introduced in some of the clusters, and a researcher additionally has information about the pretreatment periods. I argue that in such environments we need to deal with two different sources of bias: selection and technological. In my model, I employ standard methods of causal inference to address the selection problem and use pretreatment information to eliminate the technological bias. In case of one-dimensional heterogeneity, identification is achieved under natural monotonicity assumptions. The situation is considerably more complicated in case of multidimensional heterogeneity where I propose three different approaches to identification using results from transportation theory. The second essay is co-authored with Guido Imbens. We develop a new estimator for the average treatment effect in the observational studies with unobserved cluster-level heterogeneity. We show that under particular assumptions on the sampling scheme the unobserved confounders can be integrated out conditioning on the empirical distribution of covariates and policy variable within the cluster. To make this result practical we impose a particular exponential family structure that implies that a low-dimensional sufficient statistic can summarize the empirical distribution. Then we use modern causal inference methods to construct a novel doubly robust estimator. The proposed estimator uses the estimated propensity score to adjust the familiar fixed effect estimator.
Author: Rahul Singh (Econometrician)
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Languages : en
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The traditional tools of econometrics may be inadequate for modern data sets, for example the 2020 US Census, which will be deliberately corrupted by the Census Bureau in the interest of privacy. Meanwhile, the modern tools of machine learning may be inadequate for the traditional goals of policy evaluation, which are to measure cause and effect and to assess statistical significance. In this dissertation, I develop tools for flexible causal inference, weaving machine learning into econometrics and solving unique problems that arise at their intersection. Specifically, I work in three domains at the intersection between econometrics and machine learning: (Chapter 1) causal inference with privacy protected data, (Chapter 2) rigorous statistical guarantees for machine learning, and (Chapter 3) simple algorithms for complex causal problems. JEL: C81,C45,C26.
Author: Wei Xu
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ISBN: 9780438534575
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Languages : en
Pages : 192
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The dissertation consists of three chapters, with emphasis on analyzing macro- and micro-level data and applying econometric techniques so as to measure treatment effects and draw a causal inference.
Author: Marinho Angelo Bertanha
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Languages : en
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This thesis is a collection of four papers corresponding to all the research in econometrics that I have done during my graduate studies at Stanford. The first and second chapters study causal inference in regression discontinuity designs. In recent years, numerous studies have employed regression discontinuity designs with many cutoffs assigning individuals to heterogeneous treatments. A common practice is to normalize all of the cutoffs to zero and estimate only one effect. This procedure identifies the average of local treatment effects weighted by the observed relative density of individuals at the existing cutoffs. However, researchers often want to make inferences on more meaningful average treatment effects (ATE) computed over general counterfactual distributions of individuals rather than simply the observed distribution of individuals local to existing cutoffs. In the first chapter, we propose a root-n consistent and asymptotically normal estimator for such ATEs when heterogeneity follows a non-parametric smooth function of cutoff characteristics. In the case of parametric heterogeneity, observations are optimally combined to minimize the mean squared error of the ATE estimator. Inference results are also provided for the fuzzy regression discontinuity case, where the parametric heterogeneity assumption yields identification of treatment effects on individuals who comply with at least one of the multiple treatments. In the second chapter, we focus on Fuzzy Regression Discontinuity (FRD) designs with one cutoff. Many empirical studies use FRD designs to identify treatment effects when the receipt of treatment is potentially correlated to outcomes. Existing FRD methods identify the local average treatment effect (LATE) on the subpopulation of compliers with values of the forcing variable that are equal to the threshold. In the second chapter, we develop methods that assess the plausibility of generalizing LATE to subpopulations other than compliers, and to subpopulations other than those with forcing variable equal to the threshold. Specifically, we focus on testing the equality of the distributions of potential outcomes for treated compliers and always-takers, and for non-treated compliers and never-takers. We show that equality of these pairs of distributions implies that the expected outcome conditional on the forcing variable and the treatment status is continuous in the forcing variable at the threshold, for each of the two treatment regimes. As a matter of routine, we recommend that researchers present graphs with estimates of these two conditional expectations in addition to graphs with estimates of the expected outcome conditional on the forcing variable alone. We illustrate our methods using data on the academic performance of students attending the summer school program in two large school districts in the US. In the third chapter, we propose a fast resample method for two step nonlinear parametric and semiparametric models. Our resample method is faster than standard methods because it does not require recomputation of the second stage estimator during each resample iteration. The fast resample method directly exploits the score function representations computed on each bootstrap sample, thereby reducing computational time considerably. This method is used to approximate the limit distribution of parametric and semiparametric estimators, possibly simulation based, that admit an asymptotic linear representation. Monte Carlo experiments demonstrate the desirable performance and vast improvement in the numerical speed of the fast bootstrap method. Finally, the fourth chapter studies the effects of spatially correlated data on count data regressions. Count data regressions are an important tool for empirical analyses ranging from analyses of patent counts to measures of health and unemployment. Along with negative binomial, Poisson panel regressions are a preferred method of analysis because the Poisson conditional fixed effects maximum likelihood estimator (PCFE) and its sandwich variance estimator are consistent even if the data are not Poisson-distributed, or if the data are correlated over time. Analyses of counts may however also be affected by correlation in the cross-section. For example, patent counts or publications may increase across related research fields in response to common shocks. The fourth chapter shows that the PCFE and its sandwich variance estimator are consistent in the presence of such dependence in the cross-section - as long as spatial dependence is time-invariant. We develop a test for time-invariant spatial dependence and provide code in STATA and MATLAB to implement the test.
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ISBN: 9781321036053
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Languages : en
Pages : 168
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The dissertation contains three papers on causal inference in econometrics.
Author: Nikolay Doudchenko
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Languages : en
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In my dissertation I study econometric methods of causal inference with a particular focus on the use of prediction methods developed by researchers in the fields of statistical learning, machine learning, and pattern recognition. I'm also interested in the application of these methods as well as the more traditional ones to answer relevant policy questions. Chapter 1 (joint with Guido Imbens) considers the synthetic control method developed by Abadie, Diamond, Gardeazabal, and Hainmueller in several influential papers. The method is designed for estimating the effect of a treatment, in the presence of a single treated unit and a number of control units, with pre-treatment outcomes observed for all units. The method constructs a set of weights such that selected covariates and pre-treatment outcomes of the treated unit are approximately matched by a weighted average of the control units (the synthetic control). The weights are restricted to be nonnegative and sum to one. These restrictions are important partly because they make it easier for the procedure to obtain unique weights even when the number of lagged outcomes is modest relative to the number of control units, a common setting in applications. In the chapter we propose a generalization of the synthetic control procedure that allows the weights to be negative, and their sum to differ from one, and that allows for a permanent additive difference between the treated unit and the controls, similar to the difference-in-difference procedures. The weights directly minimize the distance between the lagged outcomes for the treated and the control units, using elastic net regularization to deal with a potentially large number of possible control units. In Chapter 2 (joint with Ali Yurukoglu) we quantify how bargaining power derived from firm size affects the analysis of downstream mergers and the profitability of downstream entry in the multichannel television industry. We estimate an empirical model of the industry which features negotiations between the upstream content producers and the downstream distributors of varying size. We estimate that large distributors like Comcast are able to negotiate about 25% lower content fees than smaller downstream firms such as Cablevision. We evaluate the short-run welfare effects of several recently reviewed mergers taking into account the size effects in negotiations. We also assess the degree to which size based bargaining power creates contracts which are a barrier to entry for new distributors.
Author: Timo Schenk
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ISBN: 9789036107587
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Languages : en
Pages : 0
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"This dissertation discusses and advances the econometric methods in the field of causal inference with panel data. In particular, it improves difference-in-differences methods in multiple aspects. Researchers in all fields of economics apply these methods to answer important questions, such as “what is the effect of a labor market interventions on earnings?”, “to what extent do changes in the school leaving age affect study choices?” or “by how much have the policy instruments of the clean-air-act reduced emissions of greenhouse gases?”."--
Author: Elias Moor
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Languages : en
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