Browsing by Subject "Instrumental variables"

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Essays on causal mediation analysis
(2024-05) Lee, Jung Hyub; Abrevaya, Jason; Brendan Kline; Shakeeb Khan; Haiqing Xu
This dissertation consists of three chapters on causal mediation analysis. The first two chapters propose new estimation approaches for direct and indirect effects in a semiparametric model, and the third chapter studies regression discontinuity design that incorporates indirect effects. In the first chapter, we consider a unifying framework to test for direct and indirect treatment effects in nonlinear models. Specifically, we extend a generalized linear-index model to incorporate endogenous treatments and endogenous mediators. We propose a kernel-weighted Kendall's tau statistics, which is a nonparametric rank correlation estimator, to test the significance of the direct and indirect effects of endogenous treatments on the outcome variable mediated by endogenous mediators. The proposed semiparametric model allows for treatments and mediators to be discrete, continuous, or neither of these two (e.g., censored or truncated). For the indirect effect, we construct two distinct kernel-weighted Kendall's tau statistics that capture the effect of (i) the treatment on the mediator, and (ii) the mediator on the outcome. In the second chapter, we address the issue of testing indirect effects in the generalized regression model. Also, we suggest Monte Carlo simulation results and empirical results using the new method. Unfortunately, standard joint hypothesis tests using these statistics are severely under-sized, a problem that has been noted for linear causal mediation models. To address the problem, we apply a new testing method (van Garderen and van Giersbergen (2020)) that has correct size. As an empirical illustration, we assess the effect of education level on social functioning mediated by individual income, using the British Household Panel Survey data. In the third chapter, we suggest a sharp regression discontinuity model that allows an indirect effect from the intermediate characteristics. These characteristics are determined by the treatment status and random type of each individual. We analyze RD treatment effect represented as a weighted average form and the mean expected outcome at the cutoff in the presence of intermediate characteristics. Especially, we derive bounds on the treatment effect under inequality restrictions on expected outcomes conditioned on the intermediate characteristics. Finally, we suggest a simple extension of the estimation method that incorporates the indirect effect based on local linear regression. Monte Carlo experiments validate the finite sample performance of the suggested estimation method.
Income, policy, and stable center-based child care : towards reducing the achievement gap
(2020-08-14) Caramanis, Christina Nefeli; Osborne, Cynthia Anne, 1969-; Crosnoe, Robert; von Hippel, Paul; Gershoff, Elizabeth; Wong, Patrick
Formal child care settings, such as center-based care, are known to increase school readiness, especially among disadvantaged and vulnerable populations. Yet, no research to date has empirically tested the causal link between income and center-based care among economically disadvantaged populations. In my dissertation, I answer this call by applying an instrumental variables strategy to analysis of longitudinal data from the Fragile Families and Child Wellbeing Study (FFCWS) where I leverage state variation in access to state Earned Income Tax Credits as a quasi-experimental instrument for income. My findings, which suggest a causal link between income and the use of center-based child care, represent an important policy-relevant tool by which economic support can foster enhanced early educational experiences that may have important implications for long-term patterns of attainment, achievement, and population health. This study highlights the importance of considering the influence of income support policies beyond their intended scope of promoting financial security and labor market participation. As part of this dissertation, I also extend our limited knowledge of the long-term academic effects of formal child care enrollment by filling a critical gap in integrated data that concurrently tracks family background, early childhood experiences, and reliable academic outcomes. To do this, I created an original dataset linking the Texas subsample of the FFCWS with Texas administrative school records. Results from my analyses indicate population heterogeneity across indicators of school readiness, grade retention, and math and reading achievement scores. This work highlights the importance of creating integrated data systems to answer questions of both theoretical and practical importance. With a national movement towards expansion of public preschool education that is gaining momentum, understanding the long-term impact of early childhood programs is essential.
Using multisite instrumental variables to estimate treatment effects and treatment effect heterogeneity
(2020-04-29) Runyon, Christopher Ryan; Pustejovsky, James E.; Beretvas, Susan N; Sales, Adam C; Whittaker, Tiffany A
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.