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An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies
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Multivariate Continuous Blocking to Improve Political Science Experiments." Political Analysis
, 2012
"... Political scientists use randomized treatment assignments to aid causal inference in field experiments, psychological laboratories, and survey research. Political research can do considerably better than completely randomized designs, but few political science experiments combine random treatment a ..."
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Political scientists use randomized treatment assignments to aid causal inference in field experiments, psychological laboratories, and survey research. Political research can do considerably better than completely randomized designs, but few political science experiments combine random treatment assignment with blocking on a rich set of background covariates. We describe highdimensional multivariate blocking, including on continuous covariates, detail its statistical and political advantages over complete randomization, introduce a particular algorithm, and propose a procedure to mitigate unit interference in experiments. We demonstrate the performance of our algorithm in simulations and three field experiments from campaign politics and education. 1
Optimal matching with minimal deviation from fine balance in a study of obesity and surgical outcomes. Biometrics, to appear. R package finebalance
, 2012
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Attributing Effects to a GetOutTheVote Campaign Using Full Matching and Randomization Inference.” Working Paper
, 2005
"... 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 affe ..."
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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 ZX, and the role of speculation in settling them is reduced by the use of such techniques as propensity scores, 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
Matching and Propensity Scores
, 2011
"... The popularity of matching techniques has increased considerably during the last decades. They are mainly used for matching treatment and control units in order to estimate causal treatment effects from observational studies or for integrating two or more data sets that share a common subset of cova ..."
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The popularity of matching techniques has increased considerably during the last decades. They are mainly used for matching treatment and control units in order to estimate causal treatment effects from observational studies or for integrating two or more data sets that share a common subset of covariates. In focusing on causal inference with observational studies, we discuss multivariate matching techniques and several propensity score methods, like propensity score matching, subclassification, inversepropensity weighting, and regression estimation. In addition to the theoretical aspects, we give practical guidelines for implementing these techniques and discuss the conditions under which these techniques warrant a causal interpretation of the estimated treatment effect. In particular, we emphasize that the selection of covariates and their reliable measurement is more important than the choice of a specific matching strategy.
Chapter 9 Propensity Score Matching to Extract Latent Experiments from Nonexperimental Data: A Case Study
"... During the 1995–1996 academic year, investigators from the College Board surveyed a random sample of high school junior and senior SAT ® takers to probe how they had prepared for the SAT. Among other questions, students were asked ..."
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During the 1995–1996 academic year, investigators from the College Board surveyed a random sample of high school junior and senior SAT ® takers to probe how they had prepared for the SAT. Among other questions, students were asked
An algorithm for optimal tapered matching, with application to disparities in survival. J Comput Graph Stat
"... In a tapered matched comparison, one group of individuals, called the focal group, is compared to two or more nonoverlapping matched comparison groups constructed from one population in such a way that successive comparison groups increasingly resemble the focal group. An optimally tapered matching ..."
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In a tapered matched comparison, one group of individuals, called the focal group, is compared to two or more nonoverlapping matched comparison groups constructed from one population in such a way that successive comparison groups increasingly resemble the focal group. An optimally tapered matching solves two problems simultaneously: it optimally divides the single comparison population into nonoverlapping comparison groups and optimally pairs members of the focal group with members of each comparison group. We show how to use the optimal assignment algorithm in a new way to solve the optimally tapered matching problem, with implementation in R. This issue often arises in studies of groups defined by race, gender, or other categorizations such that equitable public policy might require an understanding of the mechanisms that produce disparate outcomes, where certain specific mechanisms would be judged illegitimate, necessitating reform. In particular, we use data from Medicare and the SEER Program of the National Cancer Institute as part of an ongoing study of blackwhite disparities in survival among women with endometrial cancer.
Effect Modification and Design Sensitivity in Observational Studies
"... In an observational study of treatment effects, subjects are not randomly assigned to treatment or control, so differing outcomes in treated and control groups may reflect a bias from nonrandom assignment rather than a treatment effect. After adjusting for measured pretreatment covariates, perhaps b ..."
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In an observational study of treatment effects, subjects are not randomly assigned to treatment or control, so differing outcomes in treated and control groups may reflect a bias from nonrandom assignment rather than a treatment effect. After adjusting for measured pretreatment covariates, perhaps by matching, a sensitivity analysis determines the magnitude of bias from an unmeasured covariate that would need to be present to alter the conclusions of the naive analysis that presumes adjustments eliminated all bias. larger effects tend to be less sensitive to bias than smaller effects. Other things being equal, Effect modification is an interaction between a treatment and a pretreatment covariate controlled by matching, so that the treatment effect is larger at some values of the covariate than at others. In the presence of effect modification, it is possible that results are less sensitive to bias in subgroups experiencing larger effects. Two cases are considered: (i) an a priori grouping into a few categories based on covariates controlled by matching, (ii) a grouping discovered empirically in the data at hand. In case (i), subgroup specific bounds on Pvalues are combined using the truncated product of Pvalues. In case (ii), information that is fixed under the null hypothesis of no treatment effect is used to partition matched pairs hoping to identify pairs with larger effects. The methods are evaluated using an asymptotic device, the design sensitivity, and using simulation. Sensitivity analysis for a test of the global null hypothesis of no effect is converted to sensitivity analyses for subgroup analyses using closed testing. Africa is used to illustrate. A study of an intervention to control malaria in