Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Mathematics, 1982. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND SCIENCE / Bibliography: leaves 89-93. / by Joseph Yukich. / Ph.D.

This thesis consists of three papers on matching and weighting methods for causal inference. The first paper conducts a Monte Carlo simulation study to evaluate the performance of multivariate matching methods that select a subset of treatment and control observations. The matching methods studied are the widely used nearest neighbor matching with propensity score calipers, and the more recently proposed methods, optimal matching of an optimally chosen subset and optimal cardinality matching. The main findings are: (i) covariate balance, as measured by differences in means, variance ratios, Kolmogorov-Smirnov distances, and cross-match test statistics, is better with cardinality matching since by construction it satisfies balance requirements; (ii) for given levels of covariate balance, the matched samples are larger with cardinality matching than with the other methods; (iii) in terms of covariate distances, optimal subset matching performs best; (iv) treatment effect estimates from cardinality matching have lower RMSEs, provided strong requirements for balance, specifically, fine balance, or strength-k balance, plus close mean balance. In standard practice, a matched sample is considered to be balanced if the absolute differences in means of the covariates across treatment groups are smaller than 0.1 standard deviations. However, the simulation results suggest that stronger forms of balance should be pursued in order to remove systematic biases due to observed covariates when a difference in means treatment effect estimator is used. In particular, if the true outcome model is additive then marginal distributions should be balanced, and if the true outcome model is additive with interactions then low-dimensional joints should be balanced. The second paper focuses on longitudinal studies, where marginal structural models (MSMs) are widely used to estimate the effect of time-dependent treatments in the presence of time-dependent confounders. Under a sequential ignorability assumption, MSMs yield unbiased treatment effect estimates by weighting each observation by the inverse of the probability of their observed treatment sequence given their history of observed covariates. However, these probabilities are typically estimated by fitting a propensity score model, and the resulting weights can fail to adjust for observed covariates due to model misspecification. Also, these weights tend to yield very unstable estimates if the predicted probabilities of treatment are very close to zero, which is often the case in practice. To address both of these problems, instead of modeling the probabilities of treatment, a design-based approach is taken and weights of minimum variance that adjust for the covariates across all possible treatment histories are directly found. For this, the role of weighting in longitudinal studies of treatment effects is analyzed, and a convex optimization problem that can be solved efficiently is defined. Unlike standard methods, this approach makes evident to the investigator the limitations imposed by the data when estimating causal effects without extrapolating. A simulation study shows that this approach outperforms standard methods, providing less biased and more precise estimates of time-varying treatment effects in a variety of settings. The proposed method is used on Chilean educational data to estimate the cumulative effect of attending a private subsidized school, as opposed to a public school, on students’ university admission tests scores. The third paper is centered on observational studies with multi-valued treatments. Generalizing methods for matching and stratifying to accommodate multi-valued treatments has proven to be a complex task. A natural way to address confounding in this case is by weighting the observations, typically by the inverse probability of treatment weights (IPTW). As in the MSMs case, these weights can be highly variable and produce unstable estimates due to extreme weights. In addition, model misspecification, small sample sizes, and truncation of extreme weights can cause the weights to fail to adjust appropriately for observed confounders. The conditions the weights need to satisfy in order to provide close to unbiased treatment effect estimates with a reduced variability are determined and the convex optimization problem that can be solved in polynomial time to obtain them is defined. A simulation study with different settings is conducted to compare the proposed weighting scheme to IPTW, including generalized propensity score estimation methods that also consider explicitly the covariate balance problem in the probability estimation process. The applicability of the methods to continuous treatments is also tested. The results show that directly targeting balance with the weights, instead of focusing on estimating treatment assignment probabilities, provides the best results in terms of bias and root mean square error of the treatment effect estimator. The effects of the intensity level of the 2010 Chilean earthquake on posttraumatic stress disorder are estimated using the proposed methodology.

The explicit use of networks in modelling stochastic processes such as epidemic dynamics has revolutionised research into understanding the impact of contact pattern properties, such as degree heterogeneity, preferential mixing, clustering, weighted and dynamic linkages, on how epidemics invade, spread and how to best control them. In this thesis, I worked on mean-field approximations of stochastic processes on networks with particular focus on weighted and dynamic networks. I mostly used low dimensional ordinary differential equation (ODE) models and explicit network-based stochastic simulations to model and analyse how epidemics become established and spread in weighted and dynamic networks. I begin with a paper presenting the susceptible-infected-susceptible/recovered (SIS, SIR) epidemic models on static weighted networks with different link weight distributions. This work extends the pairwise model paradigm to weighted networks and gives excellent agreement with simulations. The basic reproductive ratio, R0, is formulated for SIR dynamics. The effects of link weight distribution on R0 and on the spread of the disease are investigated in detail. This work is followed by a second paper, which considers weighted networks in which the nodal degree and weights are not independent. Moreover, two approximate models are explored: (i) the pairwise model and (ii) the edge-based compartmental model. These are used to derive important epidemic descriptors, including early growth rate, final epidemic size, basic reproductive ratio and epidemic dynamics. Whilst the first two papers concentrate on static networks, the third paper focuses on dynamic networks, where links can be activated and/or deleted and this process can evolve together with the epidemic dynamics. We consider an adaptive network with a link rewiring process constrained by spatial proximity. This model couples SIS dynamics with that of the network and it investigates the impact of rewiring on the network structure and disease die-out induced by the rewiring process. The fourth paper shows that the generalised master equations approach works well for networks with low degree heterogeneity but it fails to capture networks with modest or high degree heterogeneity. In particular, we show that a recently proposed generalisation performs poorly, except for networks with low heterogeneity and high average degree.

The following thesis is divided in two main topics. The first part studies variations of optimal prediction problems introduced in Shiryaev, Zhou and Xu (2008) and Du Toit and Peskir (2009) to a randomized terminal-time set up and different families of utility measures. The work presents optimal stopping rules that apply under different criteria, introduces a numerical technique to build approximations of stopping boundaries for fixed terminal time problems and suggest previously reported stopping rules extend to certain generalizations of measures. The second part of the thesis is concerned with analysing optimal wealth allocation techniques within a defaultable financial market similar to Bielecki and Jang (2007). It studies a portfolio optimization problem combining a continuous time jump market and a defaultable security; and presents numerical solutions through the conversion into a Markov Decision Process and characterization of its value function as a unique fixed point to a contracting operator. This work analyses allocation strategies under several families of utilities functions, and highlights significant portfolio selection differences with previously reported results.

Zhou, Pengpeng. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (leaves 76-89). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

Thesis (Ph.D.)--University of Texas at Arlington, 2009.

Thesis (Ph. D.)--University of Michigan, 1934. / Lithoprinted. "Reprinted from the Annals of Mathematical Statistics, November, 1933."

Thesis (Ph. D.)--University of California, San Diego, 2004. / Vita. Includes bibliographical references.

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