Estimating Causal Effects with Matching Methods in the Presence and Absence of Bias Cancellation

This paper explores the implications of possible bias cancellation using Rubin-style matching methods with complete and incomplete data. After reviewing the naïve causal estimator and the approaches of Heckman and Rubin to the causal estimation problem, we show how missing data can complicate the estimation of average causal effects in different ways, depending upon the nature of the missing mechanism. While - contrary to published assertions in the literature - bias cancellation does not generally occur when the multivariate distribution of the errors is symmetric, bias cancellation has been observed to occur for the case where selection into training is the treatment variable, and earnings is the outcome variable. A substantive rationale for bias cancellation is offered, which conceptualizes bias cancellation as the result of a mixture process based on two distinct individual-level decision-making models. While the general properties are unknown, the existence of bias cancellation appears to reduce the average bias in both OLS and matching methods relative to the symmetric distribution case. Analysis of simulated data under a set of difference scenarios suggests that matching methods do better than OLS in reducing that portion of bias that comes purely from the error distribution (i.e., from "selection on unobservables"). This advantage is often found also for the incomplete data case. Matching appears to offer no advantage over OLS in reducing the impact of bias due purely to selection on unobservable variables when the error variables are generated by standard multivariate normal distributions, which lack the bias-cancellation property.

Further Holographic Investigations of Big Bang Singularities

We further explore the quantum dynamics near past cosmological singularities in anisotropic Kasner-AdS solutions using gauge/gravity duality. The dual description of the bulk evolution involves N=4 super Yang-Mills on the contracting branch of an anisotropic de Sitter space and is well defined. We compute two-point correlators of Yang-Mills operators of large dimensions using spacelike geodesics anchored on the boundary. The correlator between two points separated in a direction with negative Kasner exponent p always exhibits a pole at horizon scales, in any dimension, which we interpret as a dual signature of the classical bulk singularity. We find evidence that the pole is absent at finite coupling in the dual field theory, indicating the singularity is resolved.Comment: 26 pages, 3 figures. Version 3: added discussion on the validity of the geodesic approximatio

Estimating causal effects with matching methods in the presence and absence of bias cancellation

This paper explores the implications of possible bias cancellation using Rubin-style matching methods with complete and incomplete data. After reviewing the naïve causal estimator and the approaches of Heckman and Rubin to the causal estimation problem, we show how missing data can complicate the estimation of average causal effects in different ways, depending upon the nature of the missing mechanism. While - contrary to published assertions in the literature - bias cancellation does not generally occur when the multivariate distribution of the errors is symmetric, bias cancellation has been observed to occur for the case where selection into training is the treatment variable, and earnings is the outcome variable. A substantive rationale for bias cancellation is offered, which conceptualizes bias cancellation as the result of a mixture process based on two distinct individual-level decision-making models. While the general properties are unknown, the existence of bias cancellation appears to reduce the average bias in both OLS and matching methods relative to the symmetric distribution case. Analysis of simulated data under a set of difference scenarios suggests that matching methods do better than OLS in reducing that portion of bias that comes purely from the error distribution (i.e., from “selection on unobservables”). This advantage is often found also for the incomplete data case. Matching appears to offer no advantage over OLS in reducing the impact of bias due purely to selection on unobservable variables when the error variables are generated by standard multivariate normal distributions, which lack the bias-cancellation property. (AUTHORS)

Efficient From-Point Visibility for Global Illumination in Virtual Scenes with Participating Media

Sichtbarkeitsbestimmung ist einer der fundamentalen Bausteine fotorealistischer Bildsynthese. Da die Berechnung der Sichtbarkeit allerdings äußerst kostspielig zu berechnen ist, wird nahezu die gesamte Berechnungszeit darauf verwendet. In dieser Arbeit stellen wir neue Methoden zur Speicherung, Berechnung und Approximation von Sichtbarkeit in Szenen mit streuenden Medien vor, die die Berechnung erheblich beschleunigen, dabei trotzdem qualitativ hochwertige und artefaktfreie Ergebnisse liefern

Bringing More Finality to Finality: Conditional Consent Judgments and Appellate Review

(Excerpt) Part I provides background on finality, including an overview of the final judgment rule and other statutory grants of appellate jurisdiction. Part I then discusses consent judgments, including conditional consent judgments. Part II examines the circuit splits with respect to issues of finality and the appealability of consent judgments that reserve a right to appeal. Part III presents arguments for and against strict interpretation and application of the finality requirement regarding consent judgments. Part IV argues for resolving the controversy by adopting a standard by which appellate courts uniformly recognize a consent judgment’s reservation of a right to appeal certain adverse rulings. This Note concludes by explaining how this standard achieves the goals of the federal judicial system, such as judicial economy and fairness to parties

Hypermedia Learning Objects System - On the Way to a Semantic Educational Web

While eLearning systems become more and more popular in daily education, available applications lack opportunities to structure, annotate and manage their contents in a high-level fashion. General efforts to improve these deficits are taken by initiatives to define rich meta data sets and a semanticWeb layer. In the present paper we introduce Hylos, an online learning system. Hylos is based on a cellular eLearning Object (ELO) information model encapsulating meta data conforming to the LOM standard. Content management is provisioned on this semantic meta data level and allows for variable, dynamically adaptable access structures. Context aware multifunctional links permit a systematic navigation depending on the learners and didactic needs, thereby exploring the capabilities of the semantic web. Hylos is built upon the more general Multimedia Information Repository (MIR) and the MIR adaptive context linking environment (MIRaCLE), its linking extension. MIR is an open system supporting the standards XML, Corba and JNDI. Hylos benefits from manageable information structures, sophisticated access logic and high-level authoring tools like the ELO editor responsible for the semi-manual creation of meta data and WYSIWYG like content editing.Comment: 11 pages, 7 figure

Do Cross-National Differences in the Costs of Children Generate Cross-National Differences in Fertility Rates?

Parity-specific probabilities of having a next birth are estimated from national fertility data and are compared with nation-specific costs of having children as measured by time-budget data, by attitude data from the International Social Survey Program, and by panel data on labor earnings and standard of living changes following a birth. We focus on five countries (the US, West Germany, Denmark, Italy, and the United Kingdom), whose fertility rates span the observed fertility range in the contemporary industrialized world and whose social welfare and family policies span the conceptual space of standard welfare-state typologies. Definitive conclusions are difficult because of the multiple dimensions on which child costs can be measured, the possibility that child costs affect both the quantum and the tempo of fertility, the relatively small fertility differences across industrialized nations, and the inherent small-N problem resulting from nation-level comparisons. Empirical analysis, however, supports the assertion that institutionally driven child costs affect the fertility patterns of industrialized nations.