Collective Security and the United Nations Charter: Problems and Prospects of Judicial Execution and Peace Enforcement Under International Law (The U.N. and the Gulf War)

On August 2, 1990, Iraq invaded Kuwait. As a statement of fact, Iraq\u27s aggression and annexation of Kuwait was an infringement of the principle obligation of member states under the U.N. Charter. The purpose of this document is to clarify and focus U.S. foreign policy in regard to the case held against Iraq, describing the limitations under the U.N. Charter. It is the goal of this thesis to demonstrate that the resolution of the Gulf crisis was in violation to the U.N. Charter, and that the legitimate and just means as prescribed under international law was not observed. Finally, I will conclude the argument by explaining how this violation of international law is a threat to the role of collective security and the future prosperity of a new world order

Dealing with Limited Overlap in Estimation of Average Treatment Effects

Estimation of average treatment effects under unconfounded or ignorable treatment assignment is often hampered by lack of overlap in the covariate distributions. This lack of overlap can lead to imprecise estimates and can make commonly used estimators sensitive to the choice of specification. In such cases researchers have often used informal methods for trimming the sample. In this paper we develop a systematic approach to addressing lack of overlap. We characterize optimal subsamples for which the average treatment effect can be estimated most precisely, as well as optimally weighted average treatment effects. Under some conditions the optimal selection rules depend solely on the propensity score. For a wide range of distributions a good approximation to the optimal rule is provided by the simple selection rule to drop all units with estimated propensity scores outside the range [0.1, 0.9].Average Treatment Effects, Causality, Unconfoundness, Overlap, Treatment Effect Heterogeneity

Moving the Goalposts: Addressing Limited Overlap in Estimation of Average Treatment Effects by Changing the Estimand

Estimation of average treatment effects under unconfoundedness or exogenous treatment assignment is often hampered by lack of overlap in the covariate distributions. This lack of overlap can lead to imprecise estimates and can make commonly used estimators sensitive to the choice of specification. In such cases researchers have often used informal methods for trimming the sample. In this paper we develop a systematic approach to addressing such lack of overlap. We characterize optimal subsamples for which the average treatment effect can be estimated most precisely, as well as optimally weighted average treatment effects. Under some conditions the optimal selection rules depend solely on the propensity score. For a wide range of distributions a good approximation to the optimal rule is provided by the simple selection rule to drop all units with estimated propensity scores outside the range [0.1, 0.9].average treatment effects, causality, unconfoundedness, overlap, treatment effect heterogeneity

Nonparametric Tests for Treatment Effect Heterogeneity

A large part of the recent literature on program evaluation has focused on estimation of the average effect of the treatment under assumptions of unconfoundedness or ignorability following the seminal work by Rubin (1974) and Rosenbaum and Rubin (1983). In many cases however, researchers are interested in the effects of programs beyond estimates of the overall average or the average for the subpopulation of treated individuals. It may be of substantive interest to investigate whether there is any subpopulation for which a program or treatment has a nonzero average effect, or whether there is heterogeneity in the effect of the treatment. The hypothesis that the average effect of the treatment is zero for all subpopulations is also important for researchers interested in assessing assumptions concerning the selection mechanism. In this paper we develop two nonparametric tests. The first test is for the null hypothesis that the treatment has a zero average effect for any subpopulation defined by covariates. The second test is for the null hypothesis that the average effect conditional on the covariates is identical for all subpopulations, in other words, that there is no heterogeneity in average treatment effects by covariates. Sacrificing some generality by focusing on these two specific null hypotheses we derive tests that are straightforward to implement.average treatment effects, causality, unconfoundedness, treatment effect heterogeneity

Cold-air investigation of a 4 1/2 stage turbine with stage-loading factor of 4.66 and high specific work output. 2: Stage group performance

The stage group performance of a 4 1/2 stage turbine with an average stage loading factor of 4.66 and high specific work output was determined in cold air at design equivalent speed. The four stage turbine configuration produced design equivalent work output with an efficiency of 0.856; a barely discernible difference from the 0.855 obtained for the complete 4 1/2 stage turbine in a previous investigation. The turbine was designed and the procedure embodied the following design features: (1) controlled vortex flow, (2) tailored radial work distribution, and (3) control of the location of the boundary-layer transition point on the airfoil suction surface. The efficiency forecast for the 4 1/2 stage turbine was 0.886, and the value predicted using a reference method was 0.862. The stage group performance results were used to determine the individual stage efficiencies for the condition at which design 4 1/2 stage work output was obtained. The efficiencies of stages one and four were about 0.020 lower than the predicted value, that of stage two was 0.014 lower, and that of stage three was about equal to the predicted value. Thus all the stages operated reasonably close to their expected performance levels, and the overall (4 1/2 stage) performance was not degraded by any particularly inefficient component

Moving the Goalposts: Addressing Limited Overlap in the Estimation of Average Treatment Effects by Changing the Estimand

Estimation of average treatment effects under unconfoundedness or exogenous treatment assignment is often hampered by lack of overlap in the covariate distributions. This lack of overlap can lead to imprecise estimates and can make commonly used estimators sensitive to the choice of specification. In such cases researchers have often used informal methods for trimming the sample. In this paper we develop a systematic approach to addressing such lack of overlap. We characterize optimal subsamples for which the average treatment effect can be estimated most precisely, as well as optimally weighted average treatment effects. Under some conditions the optimal selection rules depend solely on the propensity score. For a wide range of distributions a good approximation to the optimal rule is provided by the simple selection rule to drop all units with estimated propensity scores outside the range [0.1,0.9].

Nonparametric Tests for Treatment Effect Heterogeneity

A large part of the recent literature on program evaluation has focused on estimation of the average effect of the treatment under assumptions of unconfoundedness or ignorability following the seminal work by Rubin (1974) and Rosenbaum and Rubin (1983). In many cases however, researchers are interested in the effects of programs beyond estimates of the overall average or the average for the subpopulation of treated individuals. It may be of substantive interest to investigate whether there is any subpopulation for which a program or treatment has a nonzero average effect, or whether there is heterogeneity in the effect of the treatment. The hypothesis that the average effect of the treatment is zero for all subpopulations is also important for researchers interested in assessing assumptions concerning the selection mechanism. In this paper we develop two nonparametric tests. The first test is for the null hypothesis that the treatment has a zero average effect for any subpopulation defined by covariates. The second test is for the null hypothesis that the average effect conditional on the covariates is identical for all subpopulations, in other words, that there is no heterogeneity in average treatment effects by covariates. Sacrificing some generality by focusing on these two specific null hypotheses we derive tests that are straightforward to implement.

Cold air investigation of 4 1/2-stage turbine with stage loading factor of 4.66 and high specific work output. 1: Overall performance

The turbine developed design specific work output at design speed at a total pressure ratio of 6.745 with a corresponding efficiency of 0.855. The efficiency (0.855)was 3.1 points lower than the estimated efficiency quoted by the contractor in the design report and 0.7 of a point lower than that determined by a reference prediction method. The performance of the turbine, which was a forced vortex design, agreed with the performance determined by the prediction method to about the same extent as did the performance of three reference high stage loading factor turbines, which were free vortex designs

Environmental Perturbation of the Circadian Clock Disrupts Pregnancy in the Mouse

The circadian clock has been linked to reproduction at many levels in mammals. Epidemiological studies of female shift workers have reported increased rates of reproductive abnormalities and adverse pregnancy outcomes, although whether the cause is circadian disruption or another factor associated with shift work is unknown. Here we test whether environmental disruption of circadian rhythms, using repeated shifts of the light:dark (LD) cycle, adversely affects reproductive success in mice.Young adult female C57BL/6J (B6) mice were paired with B6 males until copulation was verified by visual identification of vaginal plug formation. Females were then randomly assigned to one of three groups: control, phase-delay or phase-advance. Controls remained on a constant 12-hr light:12-hr dark cycle, whereas phase-delayed and phase-advanced mice were subjected to 6-hr delays or advances in the LD cycle every 5–6 days, respectively. The number of copulations resulting in term pregnancies was determined. Control females had a full-term pregnancy success rate of 90% (11/12), which fell to 50% (9/18; p<0.1) in the phase-delay group and 22% (4/18; p<0.01) in the phase-advance group.Repeated shifting of the LD cycle, which disrupts endogenous circadian timekeeping, dramatically reduces pregnancy success in mice. Advances of the LD cycle have a greater negative impact on pregnancy outcomes and, in non-pregnant female mice, require longer for circadian re-entrainment, suggesting that the magnitude or duration of circadian misalignment may be related to the severity of the adverse impact on pregnancy. These results explicitly link disruptions of circadian entrainment to adverse pregnancy outcomes in mammals, which may have important implications for the reproductive health of female shift workers, women with circadian rhythm sleep disorders and/or women with disturbed circadian rhythms for other reasons

Predicting the Efficacy of Future Training Programs Using Past Experiences

We investigate the problem of predicting the average effect of a new training program using experiences with previous implementations. There are two principal complications in doing so. First, the population in which the new program will be implemented may differ from the population in which the old program was implemented. Second, the two programs may differ in the mix of their components. With sufficient detail on characteristics of the two populations and sufficient overlap in their distributions, one may be able to adjust for differences due to the first complication. Dealing with the second difficulty requires data on the exact treatments the individuals received. However even in the presence of differences in the mix of components across training programs comparisons of controls in both populations who were excluded from participating in any of the programs should not be affected. To investigate the empirical importance of these issues, we compare four job training pro-grams implemented in the mid-eighties in different parts of the U.S. We find that adjusting for pre-training earnings and individual characteristics removes most of the differences between control units, but that even after such adjustments, post-training earnings for trainees are not comparable. We surmise that differences in treatment components across training programs are the likely cause, and that more details on the specific services provided by these programs are necessary to predict the effect of future programs. We also conclude that effect heterogeneity, it is essential, even in experimental evaluations of training programs record pre-training earnings and individual characteristics in order to render the extrapolation of the results to different locations more credible.