Instrumental Variables and Mendelian Randomization With Invalid Instruments

Instrumental variables (IV) methods have been widely used to determine the causal effect of a treatment, exposure, policy, or an intervention on an outcome of interest. The IV method relies on having a valid instrument, a variable that is (A1) associated with the exposure, (A2) has no direct effect on the outcome, and (A3) is unrelated to the unmeasured confounders associated with the exposure and the outcome. However, in practice, finding a valid instrument, especially those that satisfy (A2) and (A3), can be challenging. For example, in Mendelian randomization studies where genetic markers are used as instruments, complete knowledge about instruments\u27 validity is equivalent to complete knowledge about the involved genes\u27 functions. The dissertation explores the theory, methods, and application of IV methods when invalid instruments are present. First, when we have multiple candidate instruments, we establish a theoretical bound whereby causal effects are only identified as long as less than 50% of instruments are invalid, without knowing which of the instruments are invalid. We also propose a fast penalized method, called sisVIVE, to estimate the causal effect. We find that sisVIVE outperforms traditional IV methods when invalid instruments are present both in simulation studies as well as in real data analysis. Second, we propose a robust confidence interval under the multiple invalid IV setting. This work is an extension of our work on sisVIVE. However, unlike sisVIVE which is robust to violations of (A2) and (A3), our confidence interval procedure provides honest coverage even if all three assumptions, (A1)-(A3), are violated. Third, we study the single IV setting where the one IV we have may actually be invalid. We propose a nonparametric IV estimation method based on full matching, a technique popular in causal inference for observational data, that leverages observed covariates to make the instrument more valid. We propose an estimator along with inferential results that are robust to mis-specifications of the covariate-outcome model. We also provide a sensitivity analysis should the instrument turn out to be invalid, specifically violate (A3). Fourth, in application work, we study the causal effect of malaria on stunting among children in Ghana. Previous studies on the effect of malaria and stunting were observational and contained various unobserved confounders, most notably nutritional deficiencies. To infer causality, we use the sickle cell genotype, a trait that confers some protection against malaria and was randomly assigned at birth, as an IV and apply our nonparametric IV method. We find that the risk of stunting increases by 0.22 (95% CI: 0.044,1) for every malaria episode and is sensitive to unmeasured confounders

An energy efficient routing scheme by using GPS information for wireless sensor networks

In the process of transmission in wireless sensor networks (WSN), a vital problem is that a centre region close to the sink will form in which sensors have to cost vast amount of energy. To communicate in an energy-efficient manner, compressed sensing (CS) has been employed gradually. However, the performance of plain CS is significantly dependant on the specific data gathering strategy in practice. In this paper, we propose an energy-efficient data gathering scheme based on regionalisation CS. Subsequently, advanced methods for practical applications are considered. Experiments reveal that our scheme outperforms distributed CS, the straight forward and the mixed schemes by comparing different parameters of the data package, and the considered methods also guarantee its feasibility.N/

A Groupwise Approach for Inferring Heterogeneous Treatment Effects in Causal Inference

There is a growing literature in nonparametric estimation of the conditional average treatment effect given a specific value of covariates. However, this estimate is often difficult to interpret if covariates are high dimensional and in practice, effect heterogeneity is discussed in terms of subgroups of individuals with similar attributes. The paper propose to study treatment heterogeneity under the groupwise framework. Our method is simple, only based on linear regression and sample splitting, and is semiparametrically efficient under assumptions. We also discuss ways to conduct multiple testing. We conclude by reanalyzing a get-out-the-vote experiment during the 2014 U.S. midterm elections.Comment: 65 pages including supplementary materials, 9 figures, 5 table

A cluster-based decentralized job dispatching for the large-scale cloud.

The remarkable development of cloud computing in the past few years, and its proven ability to handle web hosting workloads, is prompting researchers to investigate whether clouds are suitable to run large-scale computations. Cloud load balancing is one of the solution to provide reliable and scalable cloud services. Especially, load balancing for the multimedia streaming requires dynamic and real-time load balancing strategies. With this context, this paper aims to propose an Inter Cloud Manager (ICM) job dispatching algorithm for the large-scale cloud environment. ICM mainly performs two tasks: clustering (neighboring) and decision-making. For clustering, ICM uses Hello packets that observe and collect data from its neighbor nodes, and decision-making is based on both the measured execution time and network delay in forwarding the jobs and receiving the result of the execution. We then run experiments on a large-scale laboratory test-bed to evaluate the performance of ICM, and compare it with well-known decentralized algorithms such as Ant Colony, Workload and Client Aware Policy (WCAP), and the Honey-Bee Foraging Algorithm (HFA). Measurements focus in particular on the observed total average response time including network delay in congested environments. The experimental results show that for most cases, ICM is better at avoiding system saturation under the heavy load.N/