4D Tropospheric Tomography using GPS Estimated Slant Delays

Tomographic techniques are successfully applied to obtain 4D images of the tropospheric refractivity in a local dense network. In the lower atmosphere both the small height and time scales and the non-dispersive nature of tropospheric delays require a more careful analysis of the data. We show how GPS data is processed to obtain the tropospheric slant delays using the GIPSY-OASIS II software and define the concept of pseudo-wet delays, which will be the observables in the tomographic software. We then discuss the inverse problem in the 3D stochastic tomography, using simulated refractivity fields to test the system and the impact of noise. Finally, we use data from the Kilauea network in Hawaii and a local 4x4x41-voxel grid on a region of 400 Km$^2$ and 15 Km in height to produce 4D refractivity fields. Results are compared with ECMWF forecast.Comment: 9 pages, 6 figures (2 color

Partial Identification of Local Average Treatment Effects with an Invalid Instrument

We derive nonparametric bounds for local average treatment effects without requiring the exclusion restriction assumption to hold or an outcome with a bounded support. Instead, we employ assumptions requiring weak monotonicity of mean potential outcomes within or across subpopulations defined by the values of the potential treatment status under each value of the instrument. We illustrate the identifying power of the bounds by analyzing the effect of attaining a GED, high school, or vocational degree on subsequent employment and weekly earnings using randomization into a training program as an invalid instrument.causal inference, instrumental variables, treatment effects, nonparametric bounds, principal stratification

Nonparametric Partial Identification of Causal Net and Mechanism Average Treatment Effects

When analyzing the causal e§ect of a treatment on an outcome it is important to un- derstand the mechanisms or channels through which the treatment works. In this paper we study net and mechanism average treatment e§ects (NATE and MATE, respectively), which provide an intuitive decomposition of the total average treatment e§ect (ATE) that enables learning about how the treatment a§ects the outcome. We derive informative non- parametric bounds for these two e§ects allowing for heterogeneous e§ects and without re- quiring the use of an instrumental variable or having an outcome with bounded support. We employ assumptions requiring weak monotonicity of mean potential outcomes within or across subpopulations deÖned by the potential values of the mechanism variable under each treatment arm. We illustrate the identifying power of our bounds by analyzing what part of the ATE of a training program on weekly earnings and employment is due to the obtainment of a GED, high school, or vocational degree.causal inference, treatment effects, net effects, direct effects, nonparametric bounds, principal stratification

Computability of the causal boundary by using isocausality

Recently, a new viewpoint on the classical c-boundary in Mathematical Relativity has been developed, the relations of this boundary with the conformal one and other classical boundaries have been analyzed, and its computation in some classes of spacetimes, as the standard stationary ones, has been carried out. In the present paper, we consider the notion of isocausality given by Garc\'ia-Parrado and Senovilla, and introduce a framework to carry out isocausal comparisons with standard stationary spacetimes. As a consequence, the qualitative behavior of the c-boundary (at the three levels: point set, chronology and topology) of a wide class of spacetimes, is obtained.Comment: 44 pages, 5 Figures, latex. Version with minor changes and the inclusion of Figure

Identification and Estimation of Causal Mechanisms and Net Effects of a Treatment under Unconfoundedness

An important goal when analyzing the causal effect of a treatment on an outcome is to understand the mechanisms through which the treatment causally works. We define a causal mechanism effect of a treatment and the causal effect net of that mechanism using the potential outcomes framework. These effects provide an intuitive decomposition of the total effect that is useful for policy purposes. We offer identification conditions based on an unconfoundedness assumption to estimate them, within a heterogeneous effect environment, and for the cases of a randomly assigned treatment and when selection into the treatment is based on observables. Two empirical applications illustrate the concepts and methods.causal inference, causal mechanisms, post-treatment variables, principal stratification

Estimation of Dose-Response Functions and Optimal Doses with a Continuous Treatment

This paper considers the continuous-treatment case and develops nonparametric estimators for the average dose-response function, the treatment level at which this function is maximized (location of the maximum), and the maximum value achieved by this function (size of the maximum). These parameters are identified by assuming that selection into different levels of the treatment is based on observed characteristics. The proposed nonparametric estimators of the location and size of the optimal dose are shown to be jointly asymptotically normal and uncorrelated. More generally, these estimators can be used to estimate the location and size of the maximum of a partial mean (Newey, 1994). To illustrate the utility of our approach, the techniques developed in the paper are used to estimate the turning point of the environmental Kuznets curve (EKC) for NOx, that is, the level of per capita income at which the emissions of NOx reach their peak and start decreasing. Finally, a Monte Carlo exercise is performed partly based on the data used in the empirical application. The results show that the nonparametric estimators of the location and size of the optimal dose developed in this paper work well in practice (especially when compared to a parametric model), in some cases even for relatively small sample sizes.Continuous Treatment, Nonparametric Estimation, Partial Means, Location and Size of the Maximum, Environmental Kuznets Curve

Unbundling the Degree Effect in a Job Training Program for Disadvantaged Youth

Government-sponsored education and training programs have the goal to enhance participants' skills so as to become more employable, productive and dependable citizens and thus alleviate poverty and decrease public dependence. While most of the literature evaluating training programs concentrates on estimating their total average treatment effect, these programs offer a variety of services to participants. Estimating the effect of these components is of importance for the design and the evaluation of labor market programs. In this paper, we employ a recent nonparametric approach to estimate bounds on the "mechanism average treatment effect" to evaluate the causal effect of attaining a high school diploma, General Education Development or vocational certificate within a training program for disadvantaged youth 16-24 (Job Corps) relative to other services pffered, on two labor outcomes: employment probability and weekly earnings. We provide these estimates for different demographic groups by race, ethnicity, gender, and two age-risk groups (youth and young adults). Our analysis depicts a positive impact of a degree attainment within the training program on employment probability and weekly earnings for the majority of its participants which in general accounts for 55 - 63 percent of the effect of the program. The heterogeneity of the key demographic subgroups is documented in the relative importance of a degree attainment and of the other services provided in Job Corps.Causal Inference, Treatment Effects, Mechanism Average Effects, Nonparametric Bounds, Potential Outcomes, Principal Stratification, Training Programs, Job Corps, Active Labor Market Policies, Labor and Human Capital, Public Economics, C14, I20, J01,