Alberto Abadie

Publications (4)2.25 Total impact

• Source

  Available from: harvard.edu

  Article: Comparative Effectiveness of Matching Methods for Causal Inference

  Gary King, Richard Nielsen, Carter Coberley, James E Pope, Aaron Wells, Alberto Abadie, Seth Hill, Kosuke Imai, John Londregan, Adam Meirowitz, Brandon Stewart, Liz Stuart
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  ABSTRACT: Matching methods for causal inference selectively prune observations from the data in order to reduce model dependence. They are successful when simultaneously maximizing balance (between the treated and control groups on the pre-treatment covariates) and the number of observations remaining in the data set. However, ex-isting matching methods either fix the matched sample size ex ante and attempt to reduce imbalance as a result of the procedure (e.g., propensity score and Mahalanobis distance matching) or fix imbalance ex ante and attempt to lose as few observations as possible ex post (e.g., coarsened exact matching and calpier-based approaches). As an alternative, we offer a simple graphical approach that addresses both criteria simultaneously and lets the user choose a matching solution from the imbalance-sample size frontier. In the process of applying our approach, we also discover that propensity score matching (PSM) often approximates random matching, both in real applications and in data simulated by the processes that fit PSM theory. Moreover, contrary to conventional wisdom, random matching is not benign: it (and thus often PSM) can degrade inferences relative to not matching at all. Other methods we study do not have these or other problems we describe. However, with our easy-to-use graphical approach, users can focus on choosing a matching solution for a particular application rather than whatever method happened to be used to generate it.
  01/2012;
• Source

  Available from: osu.edu

  Article: Means, Motive, & Opportunity in Becoming Informed About Politics: A Deliberative Field Experiment with Members of Congress and Their Constituents

  Kevin M Esterling, Michael A Neblo, Assistant Professor, David M J Lazer, Associate Professor, Alberto Abadie, Janet Box-Steffensmeier, Bear Braumoeller, Luke Keele, Gary King, William Minozzi, Craig Volden
  04/2010;
• Source

  Available from: osu.edu

  Article: Means, Motive, & Opportunity in Becoming Informed About Politics: A Deliberative Field Experiment with Members of Congress and Their Constituents

  Kevin M Esterling, Michael A Neblo, David M J Lazer, Alberto Abadie, Janet Box-Steffensmeier, Bear Braumoeller, Luke Keele, Gary King, William Minozzi, Craig Volden
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  ABSTRACT: Deliberative theorists emphasize that citizensÕ capacity to be-come informed when given a motive and the opportunity to participate in politics is important for democratic citizenship. We assess this capacity among citizens using a deliberative field experiment. In the summer of 2006, we conducted a field experiment in which we recruited twelve current members of the U.S. Congress to discuss immigration policy with randomly drawn small groups of their constituents. We find that constituents demon-strate a strong capacity to become informed in response to this opportunity. The primary mechanism for knowledge gains is subjectsÕ increased attention to policy outside the context of the experiment. This capacity for motivated learning seems to be spread widely throughout the population, in that it is unrelated to prior political knowledge.
  Public Opinion Quarterly 09/2008; 31. · 2.25 Impact Factor
• Source

  Available from: wustl.edu

  Article: Estimating Treatment Effects in the Presence of Noncompliance and Nonresponse: The Generalized Endogenous Treatment Model

  Kevin M Esterling, Michael A Neblo, David M J Lazer, Alberto Abadie, Janet Box-Steffensmeier, Bear Braumoeller, Kosuke Imai, Luke Keele, Gary King, William Minozzi, Jas Sekhon
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  ABSTRACT: If ignored, non-compliance with a treatment and nonresponse on outcome measures can bias estimates of treatment effects in a randomized experiment. To identify treatment effects in the case where compliance and response are conditioned on unobservables, we propose the parametric generalized endoge-nous treatment (GET) model. As a multilevel random effect model, GET improves on current approaches to principal stratification by incorporating behavioral responses within an experiment to measure each subjects' latent compliance type. We use Monte Carlo methods to show GET has a lower MSE for treatment effect estimates than existing approaches to principal stratifica-tion that impute, rather than measure, compliance type for subjects assigned to the control. In an application, we use data from a recent field experiment to assess whether exposure to a deliberative session with their member of Congress changes constituents' levels of internal and external efficacy. Since it conditions on subjects' latent compliance type, GET is able to test whether exposure to the treatment is ignorable after balancing on covariates via match-ing methods. We show that internally efficacious subjects disproportionately select into the deliberative sessions, and that matching apparently does not break the latent dependence between treatment compliance and outcome. The results suggest that exposure to the deliberative sessions improves external, but not internal, efficacy.
  09/2008;