Do Get-Out-the-Vote Calls Reduce Turnout? The Importance of Statistical Methods for Field Experiments

By Kosuke Imai. 2005

175 citations recorded by the source corpus.

Subfields: Education, Political Science and International Relations, Statistics and Probability

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Citations in the corpus

Multivariate and Propensity Score Matching Software with Automated Balance Optimization: The Matching Package for R (2008, unknown): “…Iwashyna 2003; Rubin 1997), economics (Abadie and Imbens 2006; Dehejia and Wahba 2002, 1999), political science (Bowers and Hansen 2005; Herron and Wand 2007; Imai 2005), sociology (Morgan and Harding 2006; Diprete and Engelhardt 2004; Winship and Morgan 1999; Smith 1997) and even law (Rubin 2001).”
Sharp bounds on the causal effects in randomized experiments with "truncation-by-death" (2008, methodology): “A similar analytical strategy can be applied to three-arm (or more) randomized trials with noncompliance (Imai, 2005; Cheng and Small, 2006) as well as randomized experiments with both noncompliance and ‘‘truncation-by-death’’ in order to derive the sharp bounds on various causal quantities of…”
Affinely invariant matching methods with discriminant mixtures of proportional ellipsoidally symmetric distributions (2006, methodology): “This paper examines the consequences of matching using affinely invariant methods when the covariate distributions are “discriminant mixtures of proportional ellipsoidally symmetric” (DMPES) distributions, a class herein defined, which generalizes the ellipsoidal symmetry class of Rubin and Thomas…”
On the Failure of the Bootstrap for Matching Estimators (2006, background): “…Wahba (1999, 2002), Galasso and Ravaillon (2003), Guarcello, Mealli, and Rosati (2003), Heckman, Ichimura and Todd (1997), Ichino and Becker (2002), Imai (2005), Jalan and Ravallion (1999), Lechner (2002), Myers, Olsen, Seftor, Young, and Tuttle (2002) Pradhan, and Rawlings (2003), Puhani (2002),…”
Attributing Effects to a Cluster-Randomized Get-Out-the-Vote Campaign (2009, methodology): “…2005), who moved to the control group treatment group subjects who mistakenly had been given a placebo message, and it is in marked contrast with Imai (2005), whose as-treated analysis compared to control only the treated, the subset of the treatment group who had actually received the…”
Robust Tree-based Causal Inference for Complex Ad Effectiveness Analysis (2015, methodology): “This method is also generalized to multi-category treatments [31] and ordinal treatments [23, 28], which have been widespread in various ﬁelds, including health care [4], social science [26], politics [20], online behavior study [14].”
Gamified Information Presentation and Consumer Adoption of Product Innovations (2017, background): “However, this approach implies that not all of those whom we intended to receive the experimental treatment might actually have received it—a common challenge in field experiments (see, e.g., Imai 2005)—and the associated possibility of selection bias (Heckman 1979).”
A Cautionary Note on the Use of Matching to Estimate Causal Effects: An Empirical Example Comparing Matching Estimates to an Experimental Benchmark (2010, methodology): “2007), and scholars have also begun to apply matching methods in this area (Hastings, et al. 2007; Imai 2005).”
MatchIt: Nonparametric Preprocessing for Parametric Causal Inference (2011, background): “…similar, such as in the case of matching treatment and control units from a ﬁeld experiment that was close to (but not fully) randomized (e.g., Imai 2005), when caliper matching will restrict the donor pool, or when you do not wish to change the quantity of interest and the parametric…”
Causal Inference With General Treatment Regimes (2004, methodology): “In fact, there exists empirical evidence that in certain situations the propensity score method produces more reliable estimates of causal effects than other estimation methods (e.g., Dehejia and Wahba 1999; Imai 2004).”