Now showing items 1-17 of 17

    • Causal Selection of Covariates in Regression Calibration for Mismeasured Continuous Exposure 

      Tang, Wenze (2023-03-14)
      Regression calibration has been used to correct for the bias in causal effect due to measurement error in continuous exposures, but no systematic discussion exists on how to determine covariates appropriate in measurement ...
    • CEM: Software for Coarsened Exact Matching 

      Iacus, Stefano; King, Gary; Porro, Giuseppe (American Statistical Association, 2009)
      This program is designed to improve causal inference via a method of matching that is widely applicable in observational data and easy to understand and use (if you understand how to draw a histogram, you will understand ...
    • Essays in Political Methodology 

      Blackwell, Matthew (2012-07-24)
      This dissertation provides three novel methodologies to the field of political science. In the first chapter, I describe how to make causal inferences in the face of dynamic strategies. Traditional causal inference methods ...
    • Essays on Causal Inference for Public Policy 

      Zajonc, Tristan (2012-08-07)
      Effective policymaking requires understanding the causal effects of competing proposals. Relevant causal quantities include proposals' expected effect on different groups of recipients, the impact of policies over time, ...
    • Estimating Individual Causal Effects 

      Lam, Patrick Kenneth (2013-10-18)
      Most empirical work focuses on the estimation of average treatment effects (ATE). In this dissertation, I argue for a different way of thinking about causal inference by estimating individual causal effects (ICEs). I ...
    • The Estimation of Causal Effects from Observational Data 

      Winship, Christopher; Morgan, Stephen L. (Annual Reviews, 1999)
      When experimental designs are infeasible, researchers must resort to the use of observational data from surveys, censuses, and administrative records. Because assignment to the independent variables of observational data ...
    • Five Studies on the Causes and Consequences of Voter Turnout 

      Fowler, Anthony George (2013-10-08)
      In advanced democracies, many citizens abstain from participating in the political process. Does low and unequal voter turnout influence partisan election results or public policies? If so, how can participation be ...
    • Generalizability Methods for Estimating Causal Population Effects 

      Degtiar, Irina (2021-07-12)
      Studies are often performed in samples that do not resemble the target populations relevant for policy, treatment, or other decisions. Much of the causal inference literature has focused on addressing internal validity ...
    • Health Insurance Coverage and Health-related Quality of Life among People Affected by HIV 

      Lemon, Tiffany La'Shay (2022-06-06)
      Health insurance coverage is one of the most salient determinants of health and health care access in the U.S., especially among people living with HIV. To achieve viral suppression and experience prolonged wellness, people ...
    • MatchIt: Nonparametric Preprocessing for Parametric Causal Inference 

      Stuart, Elizabeth A.; King, Gary; Imai, Kosuke; Ho, Daniel (University of California, Los Angeles, 2011)
      MatchIt implements the suggestions of Ho, Imai, King, and Stuart (2007) for improving parametric statistical models by preprocessing data with nonparametric matching methods. MatchIt implements a wide range of sophisticated ...
    • Optimal Transport Methods for Causal Inference, Multisample Testing, and Model Interpretation 

      Dunipace, Eric Arthur (2021-05-12)
      The manuscript discusses three topics that utilize optimal transport and related methodologies to solve problems in statistics. Chapter 2 uses the Wasserstein distance to construct interpretable approximations to complicated ...
    • Outcome-free Design of Observational Studies: Peer Influence on Smoking 

      Langenskioeld, S.; Rubin, Donald B. (JSTOR, 2008)
      For estimating causal effects of treatments, randomized experiments are appropriately considered the gold standard, although they are often infeasible for a variety of reasons. Nevertheless, nonrandomized studies can and ...
    • Statistical and Machine Learning Methods for Multi-Study Prediction and Causal Inference 

      Wang, Cathy (2022-06-27)
      In many areas of biomedical research, exponential advances in technology and facilitation of systematic data-sharing increased access to multiple studies. This dissertation proposes and compares methods to address three ...
    • Time Warp: Authorship Shapes the Perceived Timing of Actions and Events 

      Ebert, Jeffrey P.; Wegner, Daniel M. (Elsevier, 2010)
      It has been proposed that inferring personal authorship for an event gives rise to intentional binding, a perceptual illusion in which one's action and inferred effect seem closer in time than they otherwise would (Haggard, ...
    • Topics in randomized experiments: design, modeling, and power 

      Hunter, Kristen Brooke (2022-06-06)
      There are many statistical concerns in the design and analysis of randomized experiments. This dissertation considers design, modeling, and power in experiments with various types of complexity. Chapter 1 considers design: ...
    • Vaccines: Populations, Individuals and Models 

      Joshi, Keya Durga (2023-06-01)
      The 2009 H1N1 influenza and 2019 SARS-CoV-2 pandemics have highlighted the need for control measures against emerging infectious diseases. Vaccines are currently the most effective intervention against these pathogens, ...
    • WhatIF: R Software for Evaluating Counterfactuals 

      Stoll, Heather; King, Gary; Zeng, Langche (American Statistical Association, 2005)
      WhatIf is an R package that implements the methods for evaluating counterfactuals introduced in King and Zeng (2006a) and King and Zeng (2006b). It offers easy-to-use techniques for assessing a counterfactual's model ...