Large Sample Properties of Matching Estimators for Average Treatment Effects

By Alberto Abadie, Guido W. Imbens. 2004

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Citations in the corpus

Approximate residual balancing: debiased inference of average treatment effects in high dimensions (2016, methodology): “…popular methods include regression, matching, propensity score weighting and subclassification, as well as doubly robust combinations thereof (e.g. Abadie and Imbens (2006), Heckman et al. (1998), Hirano et al. (2003), Robins et al. (1994, 1995, 2017), Rosenbaum (2002), Tan (2010), Tsiatis (2007)…”
Automated versus Do-It-Yourself Methods for Causal Inference: Lessons Learned from a Data Analysis Competition (2017, methodology): “Given that the estimand of interest is the effect of the treatment on the treated, any estimation approach might be challenged by having a low percentage of controls relative to the treated (Abadie and Imbens, 2006).”
Unemployed and their caseworkers: should they be friends or foes? (2010, positive): “To be more precise, we use statistical propensity score matching with an extension to radius matching and incorporating regression-type adjustment following ideas by Rubin (1979), Abadie and Imbens (2006), and others.”
Robust Nonparametric Confidence Intervals for Regression‐Discontinuity Designs (2014, methodology): “(7)Alternatively, we could use “local sample covariances”as proposed in Abadie and Imbens (2010).”
Turning Out to Vote: The Costs of Finding and Getting to the Polling Place (2011, methodology): “In making our modifications to Sekhon’s program, we consulted Abadie et al. (2001) and Abadie and Imbens (2006).”
Evaluating Anti-Poverty Programs (2005, background): “Abadie and Imbens (2006) examine the formal properties in large samples of nearest-k neighbor matching estimators (for which the standard bootstrapping 29 The participation regression is of interest in its own right, as it provides insights into the targeting performance of the program; see, for…”
Econometric Evaluation of Social Programs, Part II: Using the Marginal Treatment Effect to Organize Alternative Econometric Estimators to Evaluate Social Programs, and to Forecast their Effects in New Environments (2007, background): “More efficient estimators weight the observations accounting for the variance [Heckman, Ichimura and Todd (1997, 1998), Heckman (1998), Hirano, Imbens and Ridder (2003), Abadie and Imbens (2006)].128 Matching assumes that conditioning on X eliminates selection bias.”
Effects of Protected Areas on Forest Cover Change and Local Communities (2014, background): “Also, we estimate Abadie and Imbens (2006) heteroskedasticity robust standard errors that account for our use of a fixed number of matches.”
Government, Venture Capital and the Growth of European High-Tech Entrepreneurial Firms (2014, background): “Abadie and Imbens (2006) claim that there is a tradeoff in estimating PSM with balanced or unbalanced numbers of control group observations.”
Estimating Treatment Effects using Multiple Surrogates: The Role of the Surrogate Score and the Surrogate Index (2016, background): “Although matching estimators are generally not efficient in settings with unconfoundedness (Rubin, 2006; Abadie and Imbens, 2006, 2016), they have a lot of intuitive appeal, and it is instructive to see how a matching strategy could be implemented in this case.”