Rejoinder: Matched Pairs and the Future of Cluster-Randomized Experiments

Rejoinder to "The Essential Role of Pair Matching in Cluster-Randomized Experiments, with Application to the Mexican Universal Health Insurance Evaluation" [arXiv:0910.3752]Comment: Published in at http://dx.doi.org/10.1214/09-STS274REJ the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Identification, Inference and Sensitivity Analysis for Causal Mediation Effects

Causal mediation analysis is routinely conducted by applied researchers in a variety of disciplines. The goal of such an analysis is to investigate alternative causal mechanisms by examining the roles of intermediate variables that lie in the causal paths between the treatment and outcome variables. In this paper we first prove that under a particular version of sequential ignorability assumption, the average causal mediation effect (ACME) is nonparametrically identified. We compare our identification assumption with those proposed in the literature. Some practical implications of our identification result are also discussed. In particular, the popular estimator based on the linear structural equation model (LSEM) can be interpreted as an ACME estimator once additional parametric assumptions are made. We show that these assumptions can easily be relaxed within and outside of the LSEM framework and propose simple nonparametric estimation strategies. Second, and perhaps most importantly, we propose a new sensitivity analysis that can be easily implemented by applied researchers within the LSEM framework. Like the existing identifying assumptions, the proposed sequential ignorability assumption may be too strong in many applied settings. Thus, sensitivity analysis is essential in order to examine the robustness of empirical findings to the possible existence of an unmeasured confounder. Finally, we apply the proposed methods to a randomized experiment from political psychology. We also make easy-to-use software available to implement the proposed methods.Comment: Published in at http://dx.doi.org/10.1214/10-STS321 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Measuring the Economic Impact of Civil War

Civil wars impose substantial costs on the domestic economy. We empirically measure the economic impact of such internal wars. The paper contributes to the existing literature both theoretically and methodologically. First, it explores the economic channels through which civil war affects growth. Previous studies have shown the negative growth effects of civil wars. We go a step further by identifying the channels through which war strips a country of its growth potential. Our argument is that civil war negatively impacts private investment through the process of portfolio substitution. Methodologically, the paper improves on both the data and statistical models used in the existing literature. Our data set includes better measurements of the intensity and scope of civil war as well as new economic and political data for the 1990s. Moreover, using a multiple imputation technique, we minimize the estimation bias due to missing data. Finally, to improve the model, we apply fixed and random effects models to the panel data. The evidence gives strong support to our argument indicating that the driving force behind the negative effects of civil war on economic growth is a decrease in private investment.civil war, instability, economic growth, investment, fiscal balance

MNP: R Package for Fitting the Multinomial Probit Model

MNP is a publicly available R package that fits the Bayesian multinomial probit model via Markov chain Monte Carlo. The multinomial probit model is often used to analyze the discrete choices made by individuals recorded in survey data. Examples where the multinomial probit model may be useful include the analysis of product choice by consumers in market research and the analysis of candidate or party choice by voters in electoral studies. The MNP software can also fit the model with different choice sets for each individual, and complete or partial individual choice orderings of the available alternatives from the choice set. The estimation is based on the efficient marginal data augmentation algorithm that is developed by Imai and van Dyk (2005).

eco: R Package for Ecological Inference in 2x2 Tables

eco is a publicly available R package that implements the Bayesian and likelihood methods proposed in Imai, Lu, and Strauss (2008b) for ecological inference in 2 X 2 tables as well as the method of bounds introduced by (Duncan and Davis'53). The package fits both parametric and nonparametric models using either the Expectation-Maximization algorithms (for likelihood models) or the Markov chain Monte Carlo algorithms (for Bayesian models). For all models, the individual-level data can be directly incorporated into the estimation whenever such data are available. Along with in-sample and out-of-sample predictions, the package also provides a functionality which allows one to quantify the effect of data aggregation on parameter estimation and hypothesis testing under the parametric likelihood models. This paper illustrates the usage of eco with several real data examples that are also part of the package.

Superconducting fluctuations in FeSe0.5_{0.5}Te0.5_{0.5} thin films probed via microwave spectroscopy

We investigated the microwave conductivity spectrum of FeSe$_{0.5}$Te$_{0.5}$ epitaxial films on CaF$_2$ in the vicinity of the superconducting transition. We observed the critical behavior of the superconducting fluctuations in these films with a dimensional crossover from two-dimensional to three-dimensional as the film thickness increased. From the temperature dependence of the scaling parameters we conclude that the universality class of the superconducting transition in FeSe$_{0.5}$Te$_{0.5}$ is that of the 3D-XY model. The lower limit of the onset temperature of the superconducting fluctuations, Tonset, determined by our measurements was 1.1 Tc, suggesting that the superconducting fluctuations of FeSe$_{0.5}$Te$_{0.5}$ are at least as large as those of optimally- and over-doped cuprates

Did Illegally Counted Overseas Absentee Ballots Decide the 2000 U.S. Presidential Election?

Although not widely known until much later, Al Gore received 202 more votes than George W. Bush on election day in Florida. George W. Bush is president because he overcame his election day deficit with overseas absentee ballots that arrived and were counted after election day. In the final official tally, Bush received 537 more votes than Gore. These numbers are taken from the official results released by the Florida Secretary of State’s office and so do not reflect overvotes, undervotes, unsuccessful litigation, butterfly ballot problems, recounts that might have been allowed but were not, or any other hypothetical divergence between voter preferences and counted votes. After the election, the New York Times conducted a six-month investigation and found that 680 of the overseas absentee ballots were illegally counted, and almost no one has publicly disagreed with their assessment. In this article, we describe the statistical procedures we developed and implemented for the Times to ascertain whether disqualifying these 680 ballots would have changed the outcome of the election. These include adding formal Bayesian model averaging procedures to models of ecolog- ical inference. We present a variety of new empirical results that delineate the precise conditions under which Al Gore would have been elected president and offer new evidence of the striking effectiveness of the Republican effort to prevent local election officials from applying election law equally to all Florida citizens.Governmen