Bias-Corrected Matching Estimators for Average Treatment Effects

By Alberto Abadie, Guido W. Imbens. 2011

1,839 citations recorded by the source corpus.

Subfields: Statistics and Probability, Causal Inference & Discovery

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Citations in the corpus

Machine Learning Methods That Economists Should Know About (2019, methodology): “The solution proposed in the econometrics literature, for example in the matching literature (Abadie and Imbens [2011]) is to use local linear regression, which is a regression with nearby points weighted more heavily.”
Local Linear Forests (2018, methodology): “Abadie and Imbens (2011) showed how incorporating a local linear correction improves nearest neighbor matching procedures.”
Varieties in state capitalism and corporate innovation: Evidence from an emerging economy (2021, methodology): “Then, we select the matched central and local SOEs with the nearest propensity score (Abadie and Imbens, 2011).”
Regional Policy Evaluation: Interactive Fixed Effects and Synthetic Controls (2016, positive): “specifically, linearity makes one wary of issues of interpolation and extrapolation that we shall highlight in the general framework of linear factor models as well as in the approach of synthetic controls proposed in the seminal paper by Abadie and Gardeazabal (2003). We present in the first subsec”
Matching for Balance (2019, background): “Abadie and Imbens (2011) correct the bias in nearest neighbor matching by positing a consistent regression model for the mean potential outcome function.”
Club Deals in Leveraged Buyouts (2010, positive): “The second column of Panel B contains estimated club treatment effects on premiums using the bias-corrected matching estimator of Abadie and Imbens (2007), in which the matching is conducted using the control variables in Panel A of Table 6.”
Matching on the Estimated Propensity Score (2016, background): “4), and one could consider increasing M in a particular application if such increase has a small effect on the size of the matching discrepancies, which are observed in the data (Abadie and Imbens, 2011).”
Bootstrap Inference of Matching Estimators for Average Treatment Effects (2017, methodology): “To deal with this problem, Abadie and Imbens (2011) estimated BN by B̂N = 1N N∑ i=1 (2Di − 1) × ⎛ ⎝ 1 M ∑ j∈JM (i) {μ̂(1 − Di,Xi) − μ̂(1 − Di,Xj)} ⎞ ⎠ , (2.4) where μ̂(d, x) is a nonparametric estimator ofμ(d, x).”
Organic farmers or conventional farmers: Where's the money? (2012, methodology): “In this study we use the nearest neighbor matching estimator proposed by Abadie and Imbens (2002) using “nnmatch” command in STATA.”
Entrepreneurship and Subjective Well-Being: The Mediating Role of Psychological Functioning (2019, methodology): “We also adopted a quasi-experimental approach that uses propensity score matching (PSM) to estimate the effect of being an entrepreneurship-supervisor on SWB (Abadie & Imbens, 2006; Abadie & Imbens, 2011).”
The Impact of Firm Prestige on Executive Compensation (2017, methodology): “The fifth and final estimator is the bias-adjusted matching estimator of Abadie and Imbens (2011), which adjusts for the differences in covariates between treated observations and control observations by using regression analysis.”
Globalization and Poverty in Senegal: A Worst Case Scenario? (2011, neutral): “…pairing treatment and comparison units that are similar in terms of their observable characteristics and calculating the ATE as a weighted average of the outcome difference between treated and matched controls (Abadie and Imbens, 2002; Dehejia and Wahba, 2002; Imbens 2004; Wooldridge, 2002).”