1

Paper

Opium for the Masses: How Foreign Media Can Stabilize Authoritarian Regimes Kern, Holger Hainmueller, Jens Uploaded 04-11-2007 Keywords instrumental variables causal inference local average response function LATE media effects East Germany democratization regime legitimacy Abstract In this case study of the impact of West German television on public support for the East German communist regime, we evaluate the conventional wisdom in the democratization literature that foreign mass media undermine authoritarian rule. We exploit formerly classified survey data and a natural experiment to identify the effect of foreign media exposure using instrumental variable estimators. Contrary to conventional wisdom, East Germans exposed to West German television were more satisfied with life in East Germany and more supportive of the East German regime. To explain this surprising finding, we show that East Germans used West German television primarily as a source of entertainment. Behavioral data on regional patterns in exit visa applications and archival evidence on the reaction of the East German regime to the availability of West German television corroborate this result.

2

Paper

Improving Inferences in the Study of Crisis Bargaining Arena, Phil Joyce, Kyle Uploaded 07-19-2010 Keywords crisis bargaining matching instrumental variables structural estimation empirical implications of theoretical models Abstract We present a simple crisis bargaining model that indicates that targets can generally prevent war by arming. We then create a simulated data set where the bargaining model is assumed to perfectly describe the data-generating process for those states engaged in crisis bargaining, which we assume most pairs of states are not. We further assume researchers cannot observe which states are engaged in crisis bargaining, though observable variables might serve as proxies. We demonstrate that a naive design would indicate a positive relationship between arming and war. We then evaluate the ability of matching, instrumental variables, and statistical backwards induction to uncover the true negative relationship. While each method is capable of doing so under certain conditions, each also faces important limitations. In most cases, statistical backwards induction will be the most practical of the three, but we caution that even this method is no perfect fix.

3

Paper

Beyond LATE: A Simple Method for Recovering Sample Average Treatment Effects Aronow, Peter Sovey, Allison Uploaded 03-24-2011 Keywords compliance score instrumental variables LATE average treatment effect causal inference Abstract Political scientists frequently use instrumental variables estimators to estimate the Local Average Treatment Effect (LATE), or the average treatment effect among those who comply with treatment assignment. However, the LATE is often not the causal estimand of interest; researchers may instead be interested in the Sample Average Treatment Effect (SATE), or the average treatment effect for the entire sample. We first introduce the compliance score, a pre-treatment covariate that reflects a unit's probability of treatment compliance, to researchers in political science. We posit a maximum likelihood estimation technique for predicting compliance scores even in the presence of two-sided non-compliance. We then develop a new technique, inverse compliance score weighting, that, in conjunction with a standard IV estimator, will allow researchers to easily estimate the SATE. Finally, we estimate both the LATE and SATE for a randomized experiment designed to measure the effects of media exposure and reach striking substantive conclusions.

4

Paper

Covariate Balancing Propensity Score Imai, Kosuke Ratkovic, Marc Uploaded 07-13-2012 Keywords causal inference instrumental variables inverse propensity score weighting marginal structural models observational studies propensity score matching randomized experiments Abstract The propensity score plays a central role in a variety of settings for causal inference. In particular, matching and weighting methods based on the estimated propensity score have become increasingly common in observational studies. Despite their popularity and theoretical appeal, the main practical difficulty of these methods is that the propensity score must be estimated. Researchers have found that slight misspecification of the propensity score model can result in substantial bias of estimated treatment effects. In this paper, we introduce covariate balancing propensity score (CBPS) estimation, which simultaneously optimizes the covariate balance and the prediction of treatment assignment. We exploit the dual characteristics of the propensity score as a covariate balancing score and the conditional probability of treatment assignment and estimate the CBPS within the generalized method of moments or empirical likelihood framework. We find that the CBPS dramatically improves the poor empirical performance of propensity score matching and weighting methods reported in the literature. We also show that the CBPS can be extended to a number of other important settings, including the estimation of generalized propensity score for non-binary treatments, causal inference in longitudinal settings, and the generalization of experimental and instrumental variable estimates to a target population.

5

Paper

The importance of statistical methodology for analyzing data from field experimentation: Evaluating voter mobilization strategies Imai, Kosuke Uploaded 07-08-2002 Keywords field experiments causal inference instrumental variables Abstract We introduce a set of new Markov chain Monte Carlo algorithms for Bayesian analysis of the multinomial probit model. Our Bayesian representation of the model places a new, and possibly improper, prior distribution directly on the identifiable parameters and thus is relatively easy to interpret and use. Our algorithms, which are based on the method of marginal data augmentation, involve only draws from standard distributions and dominate other available Bayesian methods in that they are as quick to converge as the fastest methods but with a more attractive prior specification.

6

Paper

Panel Effects in the American National Election Studies Bartels, Larry M. Uploaded 07-11-1998 Keywords panel attrition panel conditioning fractional pooling two-stage auxiliary instrumental variables American National Election Studies Abstract Parallel panel and fresh cross-section samples in recent NES surveys provide valuable leverage for assessing the magnitude of biases in statistical analyses of survey data due to panel attrition and panel conditioning. My analysis employing a variety of typical regression models suggests that, on average, panel biases reduce the inferential value of panel data by about ten percent. Biases in individual coefficients are rarely statistically "significant," even when panel and cross-section respondents have markedly different characteristics. Thus, while I propose adjustments for panel effects in both cross-sectional and dynamic analyses, such adjustments are unlikely to be necessary in typical applications using NES or similar data

7

Paper

Attributing Effects to A Cluster Randomized Get-Out-The-Vote Campaign: An Application of Randomization Inference Using Full Matching Bowers, Jake Hansen, Ben Uploaded 07-18-2005 Keywords causal inference randomization inference attributable effects full matching instrumental variables missing data field experiments clustering Abstract Statistical analysis requires a probability model: commonly, a model for the dependence of outcomes $Y$ on confounders $X$ and a potentially causal variable $Z$. When the goal of the analysis is to infer $Z$'s effects on $Y$, this requirement introduces an element of circularity: in order to decide how $Z$ affects $Y$, the analyst first determines, speculatively, the manner of $Y$'s dependence on $Z$ and other variables. This paper takes a statistical perspective that avoids such circles, permitting analysis of $Z$'s effects on $Y$ even as the statistician remains entirely agnostic about the conditional distribution of $Y$ given $X$ and $Z$, or perhaps even denies that such a distribution exists. Our assumptions instead pertain to the conditional distribution $Z vert X$, and the role of speculation in settling them is reduced by the existence of random assignment of $Z$ in a field experiment as well as by poststratification, testing for overt bias before accepting a poststratification, and optimal full matching. Such beginnings pave the way for ``randomization inference'', an approach which, despite a long history in the analysis of designed experiments, is relatively new to political science and to other fields in which experimental data are rarely available. The approach applies to both experiments and observational studies. We illustrate this by applying it to analyze A. Gerber and D. Green's New Haven Vote 98 campaign. Conceived as both a get-out-the-vote campaign and a field experiment in political participation, the study assigned households to treatment and desired to estimate the effect of treatment on the individuals nested within the households. We estimate the number of voters who would not have voted had the campaign not prompted them to --- that is, the total number of votes attributable to the interventions of the campaigners --- while taking into account the non-independence of observations within households, non-random compliance, and missing responses. Both our statistical inferences about these attributable effects and the stratification and matching that precede them rely on quite recent developments from statistics; our matching, in particular, has novel features of potentially wide applicability. Our broad findings resemble those of the original analysis by citet{gerbergreen00}.

8

Paper

Statistical Analysis of Randomized Experiments with Nonignorable Missing Binary Outcomes Imai, Kosuke Uploaded 07-24-2006 Keywords Causal Inference Instrumental Variables Intention-to-Treat Effect Latent Ignorability Noncompliance Treatment Effect Sensitivity Analysis Abstract Missing data are frequently encountered in the statistical analysis of randomized experiments. In this article, I propose statistical methods that can be used to analyze randomized experiments with a nonignorable missing binary outcome where the missing-data mechanism may depend on the unobserved values of the outcome variable itself. I first introduce an identification strategy for the average treatment effect and compare it with the existing alternative approaches in the literature. I then derive the maximum likelihood estimator and its asymptotic properties, and discuss possible estimation methods. Furthermore, since the proposed identification assumption is not directly verifiable from the data, I show how to conduct a sensitivity analysis based on the parameterization that links the key identification assumption with the causal quantities of interest. Then, the proposed methodology is extended to the analysis of randomized experiments with noncompliance. Although the method introduced in this article may not directly apply to randomized experiments with non-binary outcomes, I briefly discuss possible identification strategies in more general situations. Finally, I apply the proposed methodology to analyze data from the German election experiment and the influenza vaccination study, which originally motivated the methodological problems addressed in this article.

9

Paper

Model Specification in Instrumental-Variables Regression Dunning, Thad Uploaded 07-03-2008 Keywords Instrumental-Variables Least Squares (IVLS) regression model specification specification error homogenous partial effects Abstract In many applications of instrumental-variables regression, researchers seek to defend the plausibility of a key assumption: the instrumental variable is independent of the error term in a linear regression model. Although fulfilling this exogeneity criterion is necessary for a valid application of the instrumental variables approach, it is not sufficient. In the regression context, the identification of causal effects depends not just on the exogeneity of the instrument but also on the validity of the underlying model. In this paper, I focus on one feature of such models: the assumption that variation in the endogenous regressor that is related to the instrumental variable has the same effect as variation that is unrelated to the instrument. In many applications, this assumption may be quite strong, but relaxing it can limit our ability to estimate parameters of interest. After discussing two substantive examples, I develop analytic results (simulations are reported elsewhere). I also present a specification test that may be useful for determining the relevance of these issues in a given application.

10

Poster

A Comparison of Instrumental Variable Estimators in Models of Discrete Choice Quiroz Flores, Alejandro Uploaded 07-08-2010 Keywords instrumental variables discrete choice probit model continuous endogenous regressors MLE Newey Two-step GMM Abstract Comparison of three instrumental variable estimators applicable to probit models. The first estimator uses conditional probabilities and MLE. The second estimator uses Newey’s two-step minimum chi-squared estimator. A new estimator presented here uses GMM to approach probit models as non-linear regression. These models are compared in a simulation experiment. Results show that conditional probability MLE model has superior performance both in terms of bias and efficiency, although the GMM estimator follows closely.

11

Poster

Stronger Instruments by Design Morgan, Jason Keele, Luke Uploaded 07-31-2011 Keywords 2SLS instrumental variables matching non-parametric Abstract There is growing interest in natural experiments in political science. Natural experiments are often analyzed with instrumental variable estimators reflecting a belief that combining the power of natural random assignment with an instrumental variable approach will solve many of the research design problems endemic to social science. Here, we highlight how weak instruments can interact with the assumption of random assignment of the instrument. When the instrument is not randomly assigned, weak instruments produce bias that is not alleviated by additional data. We demonstrate how matching combined with a reverse caliper can be used to strengthen an instrument within a subset of the overall study. We start by presenting an alternative non-parametric instrumental variable estimator first proposed by Rosenbaum (1996) that allows us to combine matching with an IV estimator. Unlike the standard 2SLS IV estimator, this non-parametric approach provides accurate confidence intervals and consistent causal estimates even when the instrument is weak. A further advantage of this non-parametric method is the opportunity it provides to probe the random assignment assumption with a sensitivity test. We provide substantive examples of the proposed approach with a reevaluation of a recent paper that uses rainfall as an instrument for voter turnout in US counties (Hansford & Gomez 2010).

12

Poster

Hookworm Eradication as an Instrument for Schooling in the American South Henderson, John Uploaded 08-01-2011 Keywords hookworm education participation rockefeller sanitary commission instrumental variables matching permutation inference sensitivity analysis Abstract I exploit an historical natural experiment to assess whether more schooling causes greater vote participation. Specifically, I leverage the Rockefeller Sanitary Commission’s campaign to eradicate hookworm infection in the early-20th century American South as a plausibly-exogenous instrument for primary and secondary education. I evaluate two county-level interventions from the public health campaign: (a) exposure to the campaign and (b) pre-campaign hookworm incidence. Due to the presence of possible confounders, I use pair (genetic) and dose (optimal) matching techniques to strengthen the exogeneity of both instruments. I then use Rosenbaum permutation inference to assess the inclusion strength of the campaign exposure instrument, and I employ a simultaneous sensitivity analysis to evaluate robustness to remaining bias. Throughout, I find a robust and positive effect of education on participation.