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Below results based on the criteria 'Matching'
Total number of records returned: 33

1
Paper
The Varying Role of Voter Information across Democratic Societies
Sekhon, Jasjeet

Uploaded 07-26-2004
Keywords Voter Information
Elections
Causal Inference
Matching
Propensity Score Matching
Robust Estimation
Democratization
Survey Data
Abstract Using new robust matching methods for making causal inferences from survey data, I demonstrate that there are profound differences between how voters behave in mature democracies versus how they behave in new ones. The problems of voter ignorance and inattentiveness are not as serious in mature democracies as many analysts have suggested but are of grave concern in new democracies. Citizens in mature democracies are able to accomplish something that citizens in fledgling democracies are not: inattentive and poorly informed citizens are able to vote like their better informed compatriots and hence need to pay little attention to political events such as election campaigns in order to vote as if they were attentive. The results from the U.S. (which rely on various National Election Studies) and Mexico (2000 Panel Study) are reported in detail. Results from other countries are briefly reported.

2
Paper
An Observational Study of Ballot Initiatives and State Outcomes
Keele, Luke

Uploaded 07-17-2009
Keywords causal inference
matching
ballot initiatives
voter turnout
difference-in-differences
Abstract It has long been understood that the presence of the ballot initiative process leads to different outcomes among states. In general, extant research has found that the presence of ballot initiatives tends to increase voter turnout and depress state revenues and expenditures. I reconsider this possibility and demonstrate that past findings are an artifact of incorrect research design. Failure to account for differences in states often leads to a confounding association between ballot initiatives and voter turnout and fiscal policy. Here, I conduct an observational study based on a counterfactual model of inference to analyze the effects of ballot initiatives. The resulting research design leads to two analyses. First, I utilize the synthetic case control method, which allows me to compare over time outcomes in states with initiatives to states without initiatives while accounting for pretreatment baseline differences across states. Second, I use matching to assess voter turnout differences across metro areas along state boundaries with and without ballot initiatives. In both analyses, I find that ballot initiatives rarely have spillover effects on voter turnout and state fiscal policy.

3
Paper
Assessing the External Validity of Election RD Estimates: An Investigation of the Incumbency Advantage
Hainmueller, Jens
Hall, Andrew B.
Snyder, James

Uploaded 03-01-2014
Keywords RD
RDD
elections
incumbency advantage
american politics
external validity
econometrics
matching
Abstract The electoral regression discontinuity (RD) design is popular because it provides an unbiased, design-based estimate of the incumbency advantage with few assumptions. However, as is well known, the RD estimate is "local": it only identifies the effect in hypothetical districts with an exactly 50--50 tie between the Democratic and Republican candidates, and does not speak to the size of the incumbency advantage away from this threshold. There is significant uncertainty over the effect of incumbency in districts away from this threshold. Indeed, in a survey of political scientists that we administered, roughly equal numbers of respondents predict the effect to be either larger, smaller, or the same in less competitive districts. In this paper, we follow the method proposed in Angrist and Rokkanen, employing a validated Conditional Independence Assumption that, unlike in typical cases, generates directly testable implications in the context of the RD. This technique allows us to estimate the average effect of incumbency in districts in a window around the threshold as large as 15 percentage points---i.e., elections in which the winning candidate secured as much as 57.5% of the two-party vote. We find that the incumbency advantage is no larger or smaller in these less competitive districts.

4
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}.

5
Paper
Misspecification and the Propensity Score: When to Leave Out Relevant Pre-Treatment Variables
Clarke, Kevin A.
Kenkel, Brenton
Rueda, Miguel

Uploaded 07-14-2010
Keywords matching
propensity scores
conditioning
omitted variable bias
Abstract The popularity of propensity score matching has given rise to a robust, albeit informal, debate concerning the number of pre-treatment variables that should be included in the propensity score. The standard practice is to include all available pre-treatment variables in the propensity score. We demonstrate that this approach is not always optimal for the goal of reducing bias in the estimation of a treatment effect. We characterize conditions under which including an additional relevant variable in a propensity score increases the bias on the effect of interest across a variety of different implementations of the propensity score methodology. We find that matching within propensity score calipers is slightly more robust against such bias than other common methods.

6
Paper
Genetic Matching for Estimating Causal Effects: A General Multivariate Matching Method for Achieving Balance in Observational Studies
Diamond, Alexis
Sekhon, Jasjeet

Uploaded 07-19-2005
Keywords Matching
Propensity Score
Causal Inference
Genetic Algorithm
Evolutionary Programming
Optimization
Program Evaluation
Abstract Genetic matching is a new method for performing multivariate matching which uses an evolutionary search algorithm to determine the weight each covariate is given. The method utilizes an evolutionary algorithm developed by Mebane and Sekhon (1998; Sekhon and Mebane 1998) that maximizes the balance of observed potential confounders across matched treated and control units. The method is nonparametric and does not depend on knowing or estimating the propensity score, but the method is greatly improved when a known or estimated propensity score is incorporated. Genetic matching reliably reduces both the bias and the mean square error of the estimated causal effect even when the property of equal percent bias reduction (EPBR) does not hold. When this property does not hold, matching methods---such as Mahalanobis distance and propensity score matching---often perform poorly. Even if the EPBR property does hold and the propensity score is correctly specified, in finite samples, estimates based on genetic matching have lower mean square error than those based on the usual matching methods. We present a reanalysis of the LaLonde (1986) job training dataset which demonstrates the benefits of genetic matching and which helps to resolve a longstanding debate between Dehejia and Wahba (1999, 2002); Dehejia (2005) and Smith and Todd (2001, 2005a,b) over the ability of matching to overcome LaLonde's critique of nonexperimental estimators. Monte Carlos are also presented to demonstrate the properties of our method.

7
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.

8
Paper
Is It Worth Going the Extra Mile to Improve Causal Inference? Understanding Voting in Los Angeles County
Brady, Henry E.
Hui, Iris

Uploaded 07-19-2006
Keywords Counterfactual
matching
geography
GIS
voting
Abstract Two seemingly unrelated approaches to quantitative analysis have recently become more popular in social science applications. The first approach is the explicit consideration of counterfactuals in causal inference and the development of various matching techniques to choose control cases comparable to treated cases in terms of some predefined characteristics. To be useful, these methods require the identification of important characteristics that are likely to ensure that a statistical condition called “conditional independence” is met. The second trend is the increased attention given to geography and the use of spatial statistics. Although these two approaches have found their ways into the social science research separately, we think that they can be fruitfully combined. Geography and Geographic Information Systems (GIS) can improve matching and causal inference. Geography can be conceptualized in terms of “distance” and “place” which can provide guidance about potentially important characteristics that can be used to improve matching. After developing a conceptual framework that shows how this can be done, we present two empirical examples which combine counterfactual thinking with geographical ideas. The first example looks at the cost of voting and demonstrates the utility of matching using zip codes and distance to polling place. The second example looks at the performance of the InkaVote voting system in Los Angeles by matching precincts in LA with geographically adjacent precincts in surrounding counties. This example demonstrates the strengths and weaknesses of geographic proximity as a matching variable. In pursuing these examples, we also show how recent progress in GIS techniques provides tools that can deepen researchers’ understanding of their idea.

9
Paper
The Balance Test Fallacy in Matching Methods for Causal Inference
Imai, Kosuke
King, Gary
Stuart, Elizabeth

Uploaded 06-29-2006
Keywords causal inference
covariate balance
matching
treatment effect
Abstract Matching methods are widely used to adjust for possibly confounded treatment assignment when making causal inferences. The success of the matching adjustment depends on generating as much equivalence as possible between the distribution of pre-treatment covariates in the treated and control groups. In numerous articles across a diverse variety of academic fields that use matching, researchers evaluate the degree of equivalence by conducting hypothesis tests, most commonly the $t$-test for the mean difference of each of the covariates in the two matched groups. We demonstrate that these hypothesis tests are fallacious and discuss better alternatives.

10
Paper
Multivariate Matching Methods That are Monotonic Imbalance Bounding
King, Gary
Iacus, Stefano
Porro, Giuseppe

Uploaded 01-03-2011
Keywords Matching
CEM
Causal Inference
Abstract We introduce a new ``Monotonic Imbalance Bounding'' (MIB) class of matching methods for causal inference with a surprisingly large number of attractive statistical properties. MIB generalizes and extends in several new directions the only existing class, ``Equal Percent Bias Reducing'' (EPBR), which is designed to satisfy weaker properties and only in expectation. We also offer strategies to obtain specific members of the MIB class, and analyze in more detail a member of this class, called Coarsened Exact Matching, whose properties we analyze from this new perspective. We offer a variety of analytical results and numerical simulations that demonstrate how members of the MIB class can dramatically improve inferences relative to EPBR-based matching methods.

11
Paper
Alternative Balance Metrics for Bias Reduction in Matching Methods for Causal Inference
Sekhon, Jasjeet

Uploaded 07-18-2006
Keywords Matching
Causal Inference
Genetic Matching
Balance Metrics
Abstract Sekhon (2006; 2004a) and Diamond and Sekhon (2005) propose a matching method, called Genetic Matching, which algorithmically maximizes the balance of covariates between treat- ment and control observations via a genetic search algorithm (Sekhon and Mebane 1998). The method is neutral as to what measures of balance one wishes to optimize. By default, cumulative probability distribution functions of a variety of standardized statistics are used as balance metrics and are optimized without limit. The statistics are not used to conduct formal hypothesis tests, because no measure of balance is a monotonic function of bias in the estimand of interest and because we wish to maximize balance. Descriptive measures of discrepancy generally ignore key information related to bias which is captured by probability distribution functions of standardized test statistics. For example, using several descriptive metrics, one is unable reliably to recover the experimental benchmark in a testbed dataset for matching estimators (Dehejia and Wahba 1999). And these metrics, unlike those based on optimized distribution functions, perform poorly in a series of Monte Carlo sampling experiments just as one would expect given their properties.

12
Paper
Comparative Effectiveness of Matching Methods for Causal Inference
King, Gary
Nielsen, Richard

Uploaded 07-27-2011
Keywords Causal Inference
Matching
Propensity Scores
Abstract Matching is an increasingly popular method of causal inference in observational data, but applications of it are often poorly executed. We address this problem by providing a graphical approach for choosing among the numerous possible matching solutions generated by three methods: the venerable "Mahalanobis Distance Matching" (MDM), the commonly used "Propensity Score Matching" (PSM), and a newer approach called "Coarsened Exact Matching" (CEM). In the process of using our approach, we also discover that PSM often approximates random matching, both in real applications and in data simulated by the processes for which PSM theory was designed. Moreover, contrary to conventional wisdom, random matching is not benign: it (and thus PSM) can degrade inferences relative to not matching at all. We find that MDM and CEM do not have this problem, and in practice CEM usually outperforms the other two approaches. However, with our comparative graphical approach, focus is on choosing a matching solution for a particular application, which is what may improve inferences, rather than the particular method used to generate it. The easyto- follow procedures we describe thus enable researchers to improve the application of any one of these methods, to choose among them and from the various matching solutions generated by any one method, and ultimately to increase the validity and extent of causal information extracted from their data. Link to paper: http://gking.harvard.edu/files/psparadox.pdf

13
Paper
Misunderstandings among Experimentalists and Observationalists about Causal Inference
Imai, Kosuke
King, Gary
Stuart, Elizabeth

Uploaded 09-16-2007
Keywords matching
blocking
causal inference
experimental design
observational studies
average treatment effects
covariate balance
field experiments
survey experiments
Abstract We attempt to clarify, and suggest how to avoid, several serious misunderstandings about and fallacies of causal inference in experimental and observational research. These issues concern some of the most basic advantages and disadvantages of each basic research design. Problems include improper use of hypothesis tests for covariate balance between the treated and control groups, and the consequences of using randomization, blocking before randomization, and matching after treatment assignment to achieve covariate balance. Applied researchers in a wide range of scientific disciplines seem to fall prey to one or more of these fallacies, and as a result make suboptimal design or analysis choices. To clarify these points, we derive a new four-part decomposition of the key estimation errors in making causal inferences. We then show how this decomposition can help scholars from different experimental and observational research traditions better understand each other's inferential problems and attempted solutions. (This paper is forthcoming in the Journal of the Royal Statistical Society, but we have some time for revisions and would value any comments anyone might have. This is a revised and much more general version of an earlier paper, "The Balance Test Fallacy in Causal Inference".)

14
Paper
MPs for Sale? Estimating Returns to Office in Post-War British Politics
Eggers, Andrew
Hainmueller, Jens

Uploaded 03-22-2008
Keywords regression discontinuity design
RDD
matching
UK
Britain
political economy
Abstract While the role of money in policymaking is a central question in political economy research, surprisingly little attention has been given to the rents politicians actually derive from politics. We use both matching and a regression discontinuity design to analyze an original dataset on the estates of recently deceased British politicians. We find that serving in Parliament roughly doubled the wealth at death of Conservative MPs but had no discernible effect on the wealth of Labour MPs. We argue that Conservative MPs profited from office in a lax regulatory environment by using their political positions to obtain outside work as directors, consultants, and lobbyists, both while in office and after retirement. Our results are consistent with anecdotal evidence on MPs' outside financial dealings but suggest that the magnitude of Conservatives' financial gains from office was larger than has been appreciated.

15
Paper
A New Non-Parametric Matching Method for Bias Adjustment with Applications to Economic Evaluations
Sekhon, Jasjeet

Uploaded 05-11-2008
Keywords semiparametric and nonparametric matching methods
observational studies
randomized controlled trials
health economic evaluation
Abstract In health economic studies that use observational data, a key concern is how to adjust for imbalances in baseline covariates due to the non-random assignment of the programs under evaluation. Traditional methods of covariate adjustment such as regression and propensity score matching are model dependent and often fail to replicate the results of randomized controlled trials. We demonstrate a new non-parametric matching method, Genetic Matching, which is a generalization of propensity score and Mahalanobis distance matching, using two contrasting case studies. In the first, an economic evaluation of a clinical intervention (Pulmonary Artery Catheterization), applying Genetic Matching to observational data replicates the substantive results of a corresponding randomized controlled trial unlike the extant literature. And in the second case study evaluating capitation versus fee-for service, Genetic Matching radically improves balance on baseline covariates and overturns previous conclusions based on traditional methods.

16
Paper
Enhancing a Geographic Regression Discontinuity Design Through Matching to Estimate the Effect of Ballot Initiatives on Voter Turnout
Keele, Luke
Titiunik, Rocio
Zubizarreta, Jose

Uploaded 07-13-2012
Keywords matching
causal inference
geopgraphy
regression discontinuity
Abstract Of late there has been a renewed interest in natural experiments as a method for drawing causal inferences from observational data. One form of natural experiment exploits variation in geography where units in one geographic area receive a treatment while units in another area do not. In this kind of geographic natural experiment, the hope is that assignment to treatment via geographic location creates as-if random variation in treatment assignment. When this happens, adjustment for baseline covariates is unnecessary. In many applications, however, some adjustment for baseline covariates may be necessary due to strategic sorting around the border between treatment and control areas. As such, analysts may wish to combine identification strategies--using both spatial proximity and covariates--for more plausible inferences. Here we explore how to utilize spatial proximity as well as covariates in the analysis of geographic natural experiments. We contend that standard statistical tools are ill-equipped to exploit covariates as well as variation in treatment assignment that is a function of spatial proximity. We use a mixed integer programming matching algorithm to flexibly incorporate information about both the discontinuity and observed covariates which allows us to minimize spatial distance while preserving balance on observed covariates. We argue the combining both information about covariates and the discontinuity creates a method of estimation that can be informally thought of as doubly robust. We demonstrate the method with data on ballot initiatives and turnout in Milwaukee, WI.

17
Paper
Matching for Causal Inference Without Balance Checking
Iacus, Stefano
King, Gary
Porro, Giuseppe

Uploaded 06-26-2008
Keywords Matching
causal inference
observational data
missing data

Abstract We address a major discrepancy in matching methods for causal inference in observational data. Since these data are typically plentiful, the goal of matching is to reduce bias and only secondarily to keep variance low. However, most matching methods seem designed for the opposite problem, guaranteeing sample size ex ante but limiting bias by controlling for covariates through reductions in the imbalance between treated and control groups only ex post and only sometimes. (The resulting practical difficulty may explain why many published applications do not check whether imbalance was reduced and so may not even be decreasing bias.) We introduce a new class of "Monotonic Imbalance Bounding" (MIB) matching methods that enables one to choose a fixed level of maximum imbalance, or to reduce maximum imbalance for one variable without changing it for the others. We then discuss a specific MIB method called "Coarsened Exact Matching" (CEM) which, unlike most existing approaches, also explicitly bounds through ex ante user choice both the degree of model dependence and the causal effect estimation error, eliminates the need for a separate procedure to restrict data to common support, meets the congruence principle, is approximately invariant to measurement error, works well with modern methods of imputation for missing data, is computationally efficient even with massive data sets, and is easy to understand and use. This method can improve causal inferences in a wide range of applications, and may be preferred for simplicity of use even when it is possible to design superior methods for particular problems. We also make available open source software which implements all our suggestions.

18
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.

19
Paper
Adjusting Experimental Data
Keele, Luke
McConnaughy, Corrine
White, Ismail

Uploaded 07-06-2008
Keywords Experiments
matching
ANCOVA
blocking
Abstract Randomization in experiments allows researchers to assume that the treatment and control groups are balanced with respect to all characteristics except the treatment. Randomization, however, only makes balance probable, and accidental covariate imbalance can occur for any specific randomization. As such, statistical adjustments for accidental imbalance are common with experimental data. The most common method of adjustment for accidental imbalance is to use least squares to estimate the analysis of covariance (ANCOVA) model. ANCOVA, however, is a poor choice for the adjustment of experimental data. It has a strong functional form assumption, and the least squares estimator is notably biased in sample sizes of less than 500 when applied to the analysis of treatment effects. We evaluate alternative methods of adjusting experimental data. We compare ANCOVA to two different techniques. The first technique is a modified version of ANCOVA that relaxes the strong functional form assumption of this model. The second technique is matching, and we test the differences between two matching methods. For the first, we match subjects and then randomize treatment across pairs. For the second, we randomize the treatment and match prior to the estimation of treatment effects. We use all three techniques with data from a series of experiments on racial priming. We find that matching substantially increases the efficiency of experimental designs.

20
Paper
On the Use of Linear Fixed E ects Regression Models for Causal Inference
Imai, Kosuke
Kim, In Song

Uploaded 07-23-2012
Keywords difference-in-differences
first difference
matching
observational data
panel data
propensity score
randomized experiments
stratification
Abstract Linear fixed effects regression models are a primary workhorse for causal inference among applied researchers. And yet, it has been shown that even when the treatment is exogenous within each unit, the linear regression models with unit-specific fixed effects may not consistently estimate the average treatment effect. In this paper, we offer a simple solution. Specifically, we show that weighted linear fixed effects regression models can accomodate a number of identification strategies including matching, stratification, first difference, propensity score weighting, and difference-in-differences. We prove the results by establishing finite sample equivalence relationships between weighted fixed effects and these estimators. Our analysis identifies the information implicitly used by standard fixed effects models to estimate counterfactual outcomes necessary for causal inference, highlighting the potential sources of their bias and inefficiency. In addition, we develop efficient computation strategies, model-based standard errors, and a specification test for weighted fixed effects estimators. Finally, we illustrate the proposed methodology by revisiting the controversy concerning the effects of the General Agreement on Tariffs and Trade (GATT) membership on international trade. Open-source software is available for fitting the proposed weighted linear fixed effects estimators.

21
Paper
Learning from the Campaign Context: Multivariate Matching with Exposure
Christenson, Dino

Uploaded 07-14-2008
Keywords multivariate matching
non-bipartite matching
signed rank test
sensitivity analysis
political information
presidential campaigns
Abstract PolMeth XXV poster.

22
Paper
The 2011 Debt Ceiling Crisis and the 2012 House Elections: A Research Design
Monogan, Jamie

Uploaded 11-06-2012
Keywords registration
causal inference
coarsened exact matching
congressional elections
Abstract On August 1, 2011, the House of Representatives voted to raise the federal debt ceiling as well as to make cuts in discretionary spending. Although this vote allowed the federal government to avoid default, raising the debt ceiling was unpopular with the public and the vote cut across party lines. This paper proposes a research design for evaluating the effect of a House member's vote on the debt ceiling on two outcomes: the member's ability to retain his or her seat through the 2012 general election, and the incumbent's share of the two-party vote for members who face a general election competitor.

23
Paper
Exploiting a Rare Shift in Communication Flows to Document News Media Persuasion: The 1997 United Kingdom General Election
Ladd, Jonathan
Lenz, Gabriel

Uploaded 07-30-2008
Keywords Media persuasion
endorsements
campaigns
elections
matching
causal inference
Abstract Using panel data and matching techniques, we exploit a rare change in communication flows -- the endorsement switch to the Labour Party by several prominent British newspapers before the 1997 United Kingdom general election -- to study the persuasive power of the news media. These unusual events provide an opportunity to test for news media persuasion while avoiding methodological pitfalls that have plagued previous studies. By comparing readers of newspapers that switched endorsements to similar individuals who did not read these newspapers, we estimate that these papers persuaded a considerable share of their readers to vote for Labour. Depending on the statistical approach, the point estimates vary from about 10 percent to as high as 25 percent of readers. These findings provide rare, compelling evidence that the news media exert a powerful influence on mass political behavior.

24
Paper
Foreign Media and Protest Diffusion in Authoritarian Regimes: The Case of the 1989 East German Revolution
Kern, Holger

Uploaded 11-25-2008
Keywords Germany
media
causal inference
matching
authoritarian
collective action
social movement
Abstract Does access to foreign media facilitate the diffusion of protest in authoritarian regimes? Apparently for the first time, I test this hypothesis by exploiting a natural experiment in communist East Germany. I take advantage of the fact that West German television broadcasts could be received in most but not all parts of East Germany and conduct a matched analysis in which counties without access to West German television are matched to a comparison group of counties with West German television. Comparing these two groups of East German counties, I find no evidence that West German television affected the speed or depth of protest diffusion during the 1989 East German revolution.

25
Paper
The political consequences of transitions out of marriage in Great Britain
Kern, Holger

Uploaded 11-20-2007
Keywords causal inference
matching
Great Britain
marriage
divorce
widowhood
turnout
Abstract This paper uses British Household Panel Survey data to estimate the effects of divorce and widowhood on political attitudes and political behavior. In contrast to previous research, which mostly relied on cross-sectional data, a matched propensity score analysis does not find any effects of transitions out of marriage on policy preferences, party identification, and vote choice. The results also show that divorce (but not widowhood) substantially reduces electoral participation. Some preliminary evidence suggests that this effect of divorce on turnout is partially attributable to the increased residential mobility that accompanies divorce.

26
Paper
Measuring the Effects of Voter Confidence on Political Participation
Levin, Ines
Alvarez, R. Michael

Uploaded 06-22-2009
Keywords voter confidence
turnout
participation
mexico
matching
causal effects
Abstract In this paper we study the causal effect of voter confidence on participation decisions in the 2006 Mexican Election. Previous research has shown that voter confidence was a relevant factor in explaining participation during the years of the PRI hegemony. An open question is whether this relationship is still significant after the democratic transition taking place in the years 1997-2000. Moreover, in the previous literature, this problem was studied in a regression framework. In this article we argue that, since voter confidence and participation decisions are affected by similar covariates, a regression approach may lead to results which are too model dependent, and do not account for the heterogeneity of effects across voters. To solve this problem, we use matching methods, and find that voter confidence has considerable effects on participation decisions, but substantially different in magnitude from those found using the usual regression approach.

27
Poster
Optimally Selecting Matched Samples
Nielsen, Rich

Uploaded 07-20-2010
Keywords Matching
CEM
Propensity Score
Calipers
Abstract We apply a new and simple graphical method (the ``space graph"; Iacus, King, and Porro, 2010) for evaluating many matched samples and selecting the best one(s). We then use this technique to reveal patterns in the relative performance of matching methods across data sets. We also identify an important and previously unnoticed problem that causes propensity score matching with calipers to fail in precisely the applications for which it was designed.

28
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).

29
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.

30
Poster
Stronger Instruments By Design
Keele, Luke
Morgan, Jason

Uploaded 07-15-2013
Keywords matching
instrumental variables
causal inference
turnout
Abstract Natural experiments provide one means for credibly estimating causal effects with observational data. The instrumental variable (IV) method is often applied to natural experiment reflecting a belief that combining the power of natural random assignment with an instrumental variable approach will solve many of the problems endemic to observational data. While IV analysis can be quite powerful, they also rest on a series of strong assumptions that may not be credible within a specific natural experiment. Here, we highlight how the bias from weak instruments is amplified when instruments are not as-if randomly assigned. We demonstrate how using an IV estimator based on matching and randomization inference can both correct for departures from as-if random assignment and strengthen the instrument. Specifically, we combine a matching algorithm with a reverse caliper and penalties to strengthen the instrument within a subset of the overall study population. We also demonstrate how researchers can probe the random assignment of the instrument assumption with a sensitivity analysis. We provide substantive examples of the proposed approach with a reevaluation and extension of a paper that uses rainfall as an instrument for voter turnout in U.S. counties.

31
Poster
Stronger Instruments By Design
Keele, Luke
Morgan, Jason

Uploaded 07-17-2013
Keywords matching
instrumental variables
causal inference
Abstract Natural experiments provide one means for credibly estimating causal effects with observational data. The instrumental variable (IV) method is often applied to natural experiment reflecting a belief that combining the power of natural random assignment with an instrumental variable approach will solve many of the problems endemic to observational data. While IV analysis can be quite powerful, they also rest on a series of strong assumptions that may not be credible within a specific natural experiment. Here, we highlight how the bias from weak instruments is amplified when instruments are not as-if randomly assigned. We demonstrate how using an IV estimator based on matching and randomization inference can both correct for departures from as-if random assignment and strengthen the instrument. Specifically, we combine a matching algorithm with a reverse caliper and penalties to strengthen the instrument within a subset of the overall study population. We also demonstrate how researchers can probe the random assignment of the instrument assumption with a sensitivity analysis. We provide substantive examples of the proposed approach with a reevaluation and extension of a paper that uses rainfall as an instrument for voter turnout in U.S. counties.

32
Poster
Covariate Balance in Time-Series Cross-Section Data
Dimmery, Drew

Uploaded 07-22-2013
Keywords time-series cross-section
TSCS
panel
matching
balance
simulation
smoothing
causal inference
Abstract I develop and assess methods to evaluate covariate balance with panel and time-series cross-section (TSCS) data. Balance checking of this variety provides the foundation for non-parametric approaches to estimating causal effects with panel and time-series cross-section data, such as panel matching or panel reweighting. I consider a number of approaches, including a benchmark approach that simply evaluates balance on a set of lagged variables, ignoring trends. I compare this to alternative approaches that directly examine trends, including using 1) regression coefficients on a polynomial in time, 2) derivatives at the point of treatment estimated by local polynomial regression, and 3) the previous techniques re-estimated using shrinkage estimation to improve efficiency 4) parametric and non--parametric smoothing. I evaluate whether these approaches outperform the benchmark approach by borrowing strength across time periods (and across panels via shrinkage). I work with simulations and data from political science, varying various features of the data, including length of available histories and complexity of time-series trajectories.

33
Poster
Blossom: An evolutionary strategy optimizer with applications to matching, scaling, networks, and sampling
Beauchamp, Nick

Uploaded 07-24-2013
Keywords maximum likelihood
genetic algorithms
matching
multidimensional scaling
social networks
clustering
sampling methods
markov chain monte carlo
evolutionary algorithms
estimation of distribution algorithms
Abstract This paper introduces a new maximization and importance sampling algorithm, "Blossom," along with an associated R script, which is especially well suited to rugged, discontinuous, and multimodal functions where even approximate gradient methods are unfeasible, and MCMC approaches work poorly. The Blossom algorithm employs an evolutionary optimization strategy related to the Estimation of Multivariate Normal Algorithm (EMNA) or Covariance Matrix Adaptation (CMA), within the general family of Estimation of Distribution Algorithms (EDA). It works by successive iterations of sampling, selecting the highest-scoring subsample, and using the variance-covariance matrix of that subsample to generate a new sample, with various self-adapting parameters. Compared against a benchmark suite of challenging functions introduced in Yao, Liu, and Lin (1999), it finds equal or better maxima to those found by the genetic algorithm Genoud introduced in Mebane and Sekhon (2011). The algorithm is then tested in four challenging domains from political science: (1) estimation of nonlinear and multimodal spatial metrics; (2) maximizing balance for matching; (3) ideological scaling of judges with discontinuous objective functions; (4) community detection in social networks. In all of these cases, Blossom outperforms most existing nonlinear optimizers in R. Finally, the samples gathered during the optimization process can be efficiently used for importance sampling using approximate voronoi cells around sample points, equalling the performance of MCMC metropolis samplers in some circumstances, and also of use for generating efficient proposal distributions. Even in an increasingly MCMC world, there remain important roles for effective general-purpose optimizers, and Blossom is especially effective for rough terrains where most other methods fail.


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