Injuries have emerged as one of the biggest public health issues of the 21th century. Yet, the causal effects of injury control strategies are often questioned due to a lack of randomized experiments. In this thesis, a set of quasi-experimental methods are applied and discussed in the light of causal inference theory and the type of data commonly available in injury surveillance systems. I begin by defining the interrupted time series design as a special case of the regression-discontinuity design, and the method is applied to two empirical cases. The first is a ban on the sale and production of non-reduced ignition propensity (RIP) cigarettes, and the second is a tightening of the licensing rules for mopeds. A two-way fixed effects model is then applied to a case with time-varying starting dates, attempting to identify the causal effects of municipality-provided home help services for the elderly. Lastly, the effect of the Swedish bicycle helmet law is evaluated using the comparative interrupted time series and synthetic control methods. The results from the empirical studies suggest that the stricter licensing rules and the bicycle helmet law were effective in reducing injury rates, while the home help services and RIP cigarette interventions have had limited or no impact on safety as measured by fatalities and hospital admissions. I conclude that identification of the impact of injury control interventions is possible using low cost means. However, the ability to infer causality varies greatly by empirical case and method, which highlights the important role of causal inference theory in applied intervention research. While existing methods can be used with data from injury surveillance systems, additional improvements and development of new estimators specifically tailored for injury data will likely further enhance the ability to draw causal conclusions in natural settings. Implications for future research and recommendations for practice are also discussed. / Injuries have emerged as one of the biggest public health issues of the 21th century. Yet, the causal effects of injury control strategies are rarely known due to a lack of randomized experiments. In this thesis, a set of quasi-experimental methods are discussed in the light of causal inference theory and the type of data commonly available in injury surveillance systems. I begin by defining the identifying assumptions of the interrupted time series design as a special case of the regression-discontinuity design, and the method is applied to two empirical cases. The first is a ban on the sale and production of non-fire safe cigarettes and the second is a tightening of the licensing rules for mopeds. A fixed effects panel regression analysis is then applied to a case with time-varying starting dates, attempting to identify the causal effects of municipality-provided home help services for the elderly. Lastly, the causal effect of the Swedish bicycle helmet law is evaluated using a comparative interrupted time series design and a synthetic control design. I conclude that credible identification of the impact of injury control interventions is possible using simple and cost-effective means. Implications for future research and recommendations for practice are discussed.

The theories of causality put forward by Pearl and the Spirtes-Glymour-Scheines group have entered the mainstream of statistical thinking. These theories show that under ideal conditions, causal relationships can be inferred from purely statistical observational data. Nancy Cartwright advances certain arguments against these causal inference algorithms: the well-known factory example argument against the Causal Markov condition and an argument against faithfulness. We point to the dependence of the first argument on undefined categories external to the technical apparatus of causal inference algorithms. We acknowledge the possible practical implication of her second argument, yet we maintain, with respect to both arguments, that this variety of causal inference, if not universal, is nonetheless eminently useful. Cartwright argues against assumptions that are essential not only to causal inference algorithms but to causal inference generally, even if, as she contends, they are not without exception and that the same is true of other, likewise essential, assumptions. We indicate that causal inference is an iterative process and that causal inference algorithms assist, rather than replace, that process as performed by human beings.

The theories of causality put forward by Pearl and the Spirtes-Glymour-Scheines group have entered the mainstream of statistical thinking. These theories show that under ideal conditions, causal relationships can be inferred from purely statistical observational data. Nancy Cartwright advances certain arguments against these causal inference algorithms: the well-known factory example argument against the Causal Markov condition and an argument against faithfulness. We point to the dependence of the first argument on undefined categories external to the technical apparatus of causal inference algorithms. We acknowledge the possible practical implication of her second argument, yet we maintain, with respect to both arguments, that this variety of causal inference, if not universal, is nonetheless eminently useful. Cartwright argues against assumptions that are essential not only to causal inference algorithms but to causal inference generally, even if, as she contends, they are not without exception and that the same is true of other, likewise essential, assumptions. We indicate that causal inference is an iterative process and that causal inference algorithms assist, rather than replace, that process as performed by human beings.

<p>This thesis examines causal inference related topics involving intermediate variables, and uses Bayesian methodologies to advance analysis capabilities in these areas. First, joint modeling of outcome variables with intermediate variables is considered in the context of birthweight and censored gestational age analyses. The proposed methodology provides improved inference capabilities for birthweight and gestational age, avoids post-treatment selection bias problems associated with conditional on gestational age analyses, and appropriately assesses the uncertainty associated with censored gestational age. Second, principal stratification methodology for settings where causal inference analysis requires appropriate adjustment of intermediate variables is extended to observational settings with binary treatments and binary intermediate variables. This is done by uncovering the structural pathways of unmeasured confounding affecting principal stratification analysis and directly incorporating them into a model based sensitivity analysis methodology. Demonstration focuses on a study of the efficacy of influenza vaccination in elderly populations. Third, flexibility, interpretability, and capability of principal stratification analyses for continuous intermediate variables are improved by replacing the current fully parametric methodologies with semiparametric Bayesian alternatives. This presentation is one of the first uses of nonparametric techniques in causal inference analysis,</p><p>and opens a connection between these two fields. Demonstration focuses on two studies, one involving a cholesterol reduction drug, and one examine the effect of physical activity on cardiovascular disease as it relates to body mass index.</p> / Dissertation

This thesis contributes to the research field of causal inference, where the effect of a treatment on an outcome is of interest is concerned. Many such effects cannot be estimated through randomised experiments. For example, the effect of higher education on future income needs to be estimated using observational data. In the estimation, assumptions are made to make individuals that get higher education comparable with those not getting higher education, to make the effect estimable. Another assumption often made in causal inference (both in randomised an nonrandomised studies) is that the treatment received by one individual has no effect on the outcome of others. If this assumption is not met, the meaning of the causal effect of the treatment may be unclear. In the first paper the effect of college choice on income is investigated using Swedish register data, by comparing graduates from old and new Swedish universities. A semiparametric method of estimation is used, thereby relaxing functional assumptions for the data. One assumption often made in causal inference in observational studies is that individuals in different treatment groups are comparable, given that a set of pretreatment variables have been adjusted for in the analysis. This so called unconfoundedness assumption is in principle not possible to test and, therefore, in the second paper we propose a Bayesian sensitivity analysis of the unconfoundedness assumption. This analysis is then performed on the results from the first paper. In the third paper of the thesis, we study profile likelihood as a tool for semiparametric estimation of a causal effect of a treatment. A semiparametric version of the Bayesian sensitivity analysis of the unconfoundedness assumption proposed in Paper II is also performed using profile likelihood. The last paper of the thesis is concerned with the estimation of direct and indirect causal effects of a treatment where interference between units is present, i.e., where the treatment of one individual affects the outcome of other individuals. We give unbiased estimators of these direct and indirect effects for situations where treatment probabilities vary between individuals. We also illustrate in a simulation study how direct and indirect causal effects can be estimated when treatment probabilities need to be estimated using background information on individuals.

This thesis aimed to apply the causal framework with potential outcomes to examine the causal effect of air temperature on reported violent crimes in Swedish municipalities. The Generalized Estimating Equations method was used on yearly, monthly and also July only data for the time period 2002-2014. One significant causal effect was established but the majority of the results pointed to there being no causal effect between air temperature and reported violent crimes.

abstract: Mediation analysis is used to investigate how an independent variable, X, is related to an outcome variable, Y, through a mediator variable, M (MacKinnon, 2008). If X represents a randomized intervention it is difficult to make a cause and effect inference regarding indirect effects without making no unmeasured confounding assumptions using the potential outcomes framework (Holland, 1988; MacKinnon, 2008; Robins & Greenland, 1992; VanderWeele, 2015), using longitudinal data to determine the temporal order of M and Y (MacKinnon, 2008), or both. The goals of this dissertation were to (1) define all indirect and direct effects in a three-wave longitudinal mediation model using the causal mediation formula (Pearl, 2012), (2) analytically compare traditional estimators (ANCOVA, difference score, and residualized change score) to the potential outcomes-defined indirect effects, and (3) use a Monte Carlo simulation to compare the performance of regression and potential outcomes-based methods for estimating longitudinal indirect effects and apply the methods to an empirical dataset. The results of the causal mediation formula revealed the potential outcomes definitions of indirect effects are equivalent to the product of coefficient estimators in a three-wave longitudinal mediation model with linear and additive relations. It was demonstrated with analytical comparisons that the ANCOVA, difference score, and residualized change score models’ estimates of two time-specific indirect effects differ as a function of the respective mediator-outcome relations at each time point. The traditional model that performed the best in terms of the evaluation criteria in the Monte Carlo study was the ANCOVA model and the potential outcomes model that performed the best in terms of the evaluation criteria was sequential G-estimation. Implications and future directions are discussed. / Dissertation/Thesis / Doctoral Dissertation Psychology 2018

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 increased and how can the electorate become more representative of the greater population? / Government

abstract: Statistical inference from mediation analysis applies to populations, however, researchers and clinicians may be interested in making inference to individual clients or small, localized groups of people. Person-oriented approaches focus on the differences between people, or latent groups of people, to ask how individuals differ across variables, and can help researchers avoid ecological fallacies when making inferences about individuals. Traditional variable-oriented mediation assumes the population undergoes a homogenous reaction to the mediating process. However, mediation is also described as an intra-individual process where each person passes from a predictor, through a mediator, to an outcome (Collins, Graham, & Flaherty, 1998). Configural frequency mediation is a person-oriented analysis of contingency tables that has not been well-studied or implemented since its introduction in the literature (von Eye, Mair, & Mun, 2010; von Eye, Mun, & Mair, 2009). The purpose of this study is to describe CFM and investigate its statistical properties while comparing it to traditional and casual inference mediation methods. The results of this study show that joint significance mediation tests results in better Type I error rates but limit the person-oriented interpretations of CFM. Although the estimator for logistic regression and causal mediation are different, they both perform well in terms of Type I error and power, although the causal estimator had higher bias than expected, which is discussed in the limitations section. / Dissertation/Thesis / Masters Thesis Psychology 2019

Indiana University-Purdue University Indianapolis (IUPUI) / Observational data are frequently used for causal inference of treatment effects on prespecified outcomes. Several widely used causal inference methods have adopted the method of inverse propensity score weighting (IPW) to alleviate the in uence of confounding. However, the IPW-type methods, including the doubly robust methods, are prone to large variation in the estimation of causal e ects due to possible extreme weights. In this research, we developed an ordinary least-squares (OLS)-based causal inference method, which does not involve the inverse weighting of the individual propensity scores. We first considered the scenario of homogeneous treatment effect. We proposed a two-stage estimation procedure, which leads to a model-free estimator of average treatment effect (ATE). At the first stage, two summary scores, the propensity and mean scores, are estimated nonparametrically using regression splines. The targeted ATE is obtained as a plug-in estimator that has a closed form expression. Our simulation studies showed that this model-free estimator of ATE is consistent, asymptotically normal and has superior operational characteristics in comparison to the widely used IPW-type methods. We then extended our method to the scenario of heterogeneous treatment effects, by adding in an additional stage of modeling the covariate-specific treatment effect function nonparametrically while maintaining the model-free feature, and the simplicity of OLS-based estimation. The estimated covariate-specific function serves as an intermediate step in the estimation of ATE and thus can be utilized to study the treatment effect heterogeneity. We discussed ways of using advanced machine learning techniques in the proposed method to accommodate high dimensional covariates. We applied the proposed method to a case study evaluating the effect of early combination of biologic & non-biologic disease-modifying antirheumatic drugs (DMARDs) compared to step-up treatment plan in children with newly onset of juvenile idiopathic arthritis disease (JIA). The proposed method gives strong evidence of significant effect of early combination at 0:05 level. On average early aggressive use of biologic DMARDs leads to around 1:2 to 1:7 more reduction in clinical juvenile disease activity score at 6-month than the step-up plan for treating JIA.