Linear estimation of average global effects

We study the problem of estimating the average causal effect of treating every member of a population, as opposed to none, using an experiment that treats only some. This is the policy-relevant estimand when deciding whether to scale up an intervention based on the results of an RCT, for example, but differs from the usual average treatment effect in the presence of spillovers. We consider both estimation and experimental design given a bound (parametrized by $\eta > 0$) on the rate at which spillovers decay with the ``distance'' between units, defined in a generalized way to encompass spatial and quasi-spatial settings, e.g. where the economically relevant concept of distance is a gravity equation. Over all estimators linear in the outcomes and all cluster-randomized designs the optimal geometric rate of convergence is $n^{-\frac{1}{2+\frac{1}{\eta}}}$, and this rate can be achieved using a generalized ``Scaling Clusters'' design that we provide. We then introduce the additional assumption, implicit in the OLS estimators used in recent applied studies, that potential outcomes are linear in population treatment assignments. These estimators are inconsistent for our estimand, but a refined OLS estimator is consistent and rate optimal, and performs better than IPW estimators when clusters must be small. Its finite-sample performance can be improved by incorporating prior information about the structure of spillovers. As a robust alternative to the linear approach we also provide a method to select estimator-design pairs that minimize a notion of worst-case risk when the data generating process is unknown. Finally, we provide asymptotically valid inference methods

Biometric payment systems and welfare benefits

Biometric payment systems are claimed to reduce leakages in public welfare programmes. Indeed, 230 programmes in more than 80 countries are currently deploying such systems to improve security and reduce corruption and fraud. Yet, there is little evidence for their effectivenes

Brief of Defendant-Appellees Catholic Diocese of Cleveland and Bishop Anthony M. Pilla , Hawley v. City of Cleveland, 24 F3d 814 (6th Cir. 1994)

A City of Cleveland Ordinance leasing space in the airport to the Catholic Diocese of Cleveland for use as a chapel, which is available to religious groups and persons of all faiths does not violate the Establishment Clause of the First Amendment

Brief of Defendant-Appellees Catholic Diocese of Cleveland and Bishop Anthony M. Pilla , Hawley v. City of Cleveland, 24 F3d 814 (6th Cir. 1994)

A City of Cleveland Ordinance leasing space in the airport to the Catholic Diocese of Cleveland for use as a chapel, which is available to religious groups and persons of all faiths does not violate the Establishment Clause of the First Amendment

Managing Self-Confidence: Theory and Experimental Evidence

Evidence from social psychology suggests that agents process information about their own ability in a biased manner. This evidence has motivated exciting research in behavioral economics, but has also garnered critics who point out that it is potentially consistent with standard Bayesian updating. We implement a direct experimental test. We study a large sample of 656 undergraduate students, tracking the evolution of their beliefs about their own relative performance on an IQ test as they receive noisy feedback from a known data-generating process. Our design lets us repeatedly measure the complete relevant belief distribution incentive-compatibly. We find that subjects (1) place approximately full weight on their priors, but (2) are asymmetric, over-weighting positive feedback relative to negative, and (3) conservative, updating too little in response to both positive and negative signals. These biases are substantially less pronounced in a placebo experiment where ego is not at stake. We also find that (4) a substantial portion of subjects are averse to receiving information about their ability, and that (5) less confident subjects are causally more likely to be averse. We unify these phenomena by showing that they all arise naturally in a simple model of optimally biased Bayesian information processing.

Buoyant Effects on the Flammability of Silicone Samples Planned for the Spacecraft Fire Experiment (Saffire)

Flammability experiments on silicone samples were conducted in anticipation of the Spacecraft Fire Experiment (Saffire). The sample geometry was chosen to match the NASA 6001 Test 1 specification, namely 5 cm wide by 30 cm tall. Four thicknesses of silicone (0.25, 0.36, 0.61 and 1.00 mm) were examined. Tests included traditional upward buoyant flame spread using Test 1 procedures, downward opposed-flow flame spread, horizontal and angled flame spread, and forced-flow upward and downward flame spread. In addition to these configurations, upward and downward tests were conducted in a chamber with varying oxygen concentrations. In the upward buoyant flame spread tests, the flame generally did not burn the entire sample. As thickness was increased, the flame spread distance decreased before flame extinguishment. For the thickest sample, ignition could not be achieved. In the downward tests, the two thinnest samples permitted the flame to burn the entire sample, but the spread rate was lower compared to the corresponding upward values. The other two thicknesses could not be ignited in the downward configuration. The increased flammability for downward spreading flames relative to upward ones is uncommon. The two thinnest samples also burned completely in the horizontal configuration, as well as at angles up to 75 degrees from the horizontal. Upward tests in air with an added forced flow were more flammable. The upward and downward flammability behavior was compared in atmospheres of varying oxygen concentration to determine a maximum oxygen concentration for each configuration. Complementary analyses using EDS, TGA, and SEM techniques suggest the importance of the silica layer deposited downstream onto the unburned sample surface

Einsatz von Protein- und Metabolit-Profiling-Methoden zur Unterscheidung von ökologischem und konventionellem Weizen

The interest in methods to proof organic food authenticity increases with the steadily rising popularity of food labelled organic. Profiling techniques enable the detection of a wide range of substances in biological samples. Together with bioinformatics tools these techniques are useful for biomarker searching, e. g. in plant extracts. Metabolomic and proteomic profiling techniques were used to screen organic and conventional wheat, originating from the DOK field trial in Switzerland. Up to 11 wheat varieties from three harvest years were analysed. We were able to detect a number of metabolites and proteins with significant differences between samples of conventional and organic grown wheat of the variety “Runal”. Results viewed across all 11 varieties indicated a higher influence of both the variety and the seasonal effects than the cultivation form. Nevertheless, PCA performed on metabolite data for the individual varieties and for individual growing seasons revealed a clustering according to the cultivation forms. Further research is necessary to assess, whether these methods can be applied to distinguish organic and conventional wheat from agricultural practice

Buoyant Effects on the Flammability of Silicone Samples Planned for the Spacecraft Fire Experiment (Saffire)

Flammability experiments on silicone samples were conducted in anticipation of the Spacecraft Fire Experiment (Saffire). The sample geometry was chosen to match the NASA 6001 Test 1 specification, namely 5 cm wide by 30 cm tall. Four thicknesses of silicone (0.25, 0.36, 0.61 and 1.00 mm) were examined. Tests included traditional upward buoyant flame spread using Test 1 procedures, downward opposed flow flame spread, horizontal and angled flame spread, forced flow upward and downward flame spread. In addition to these configurations, upward and downward tests were also conducted in a chamber with varying oxygen concentrations. In the upward buoyant flame spread tests, the flame generally did not burn the entire sample. As thickness was increased, the flame spread distance decreased before flame extinguishment. For the thickest sample, ignition could not be achieved. In the downward tests, the two thinnest samples permitted the flame to burn the entire sample, but the spread rate was lower compared to the corresponding upward values. The other two thicknesses could not be ignited in the downward configuration. The increased flammability for downward spreading flames relative to upward ones is uncommon. The two thinnest samples also burned completely in the horizontal configuration, as well as at angles up to 75 degrees from the horizontal. The upward and downward flammability behavior was compared in atmospheres of varying oxygen concentration to determine a maximum oxygen concentration for each configuration. Upward tests in air with an added forced flow were more flammable. Complementary analyses using SEM and TGA techniques suggest the importance of the silica layer formed on the burned sample surface. As silicone burns upward, silica deposits downstream If the silicone is ignited in the downward configuration, it burns the entire length of the sample Burning upward at an angle increases the burn length in some cases possibly due to less silica deposition Forced flow in the upward burning case increases flammability, likely due to an increase in convective flow preventing silica from depositing Samples in upward configuration burning under forced flow self extinguish after forced flow is remove

Learning to classify organic and conventional wheat - a machine-learning driven approach using the MeltDB 2.0 metabolomics analysis platform

Kessler N, Bonte A, Albaum S, et al. Learning to classify organic and conventional wheat - a machine-learning driven approach using the MeltDB 2.0 metabolomics analysis platform. Frontiers in Bioinformatics and Computational Biology. 2015;3: 35.We present results of our machine learning approach to the problem of classifying GC-MS data originating from wheat grains of different farming systems. The aim is to investigate the potential of learning algorithms to classify GC-MS data to be either from conventionally grown or from organically grown samples and considering different cultivars. The motivation of our work is rather obvious on the background of nowadays increased demand for organic food in post-industrialized societies and the necessity to prove organic food authenticity. The background of our data set is given by up to eleven wheat cultivars that have been cultivated in both farming systems, organic and conventional, throughout three years. More than 300 GC-MS measurements were recorded and subsequently processed and analyzed in the MeltDB 2.0 metabolomics analysis platform, being briefly outlined in this paper. We further describe how unsupervised (t-SNE, PCA) and supervised (RF, SVM) methods can be applied for sample visualization and classification. Our results clearly show that years have most and wheat cultivars have second-most influence on the metabolic composition of a sample. We can also show, that for a given year and cultivar, organic and conventional cultivation can be distinguished by machine-learning algorithms