When to Control for Covariates? Panel Asymptotics for Estimates of Treatment Effects

By J. Angrist, Jinyong Hahn. Review of Economics and Statistics, 2004

112 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/8e5c7ef441f3a2f6dc0f4e1d062f036440dbbe89

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Citations in the corpus

Large Sample Properties of Matching Estimators for Average Treatment Effects (2004, background): “This is an appealing feature of simple matching estimators because reliance on properties of higher order derivatives has recently come under some criticism (e.g., Horowitz and Mammen, 2002; Angrist and Hahn, 2003; Robins and Ritov 1997).”
BAYESIAN ASPECTS OF TREATMENT CHOICE (2011, background): “Angrist and Hahn (2004) are motivated by the result in Hahn (1998) that knowledge of the”
Inference in Linear Regression Models with Many Covariates and Heteroscedasticity (2015, background): “For example, Angrist and Hahn (2004) discuss when to include many covariates in treatment effect models.”
Capital Flow Management Measures: What Are They Good For? (2014, background): “Also, although propensity-score matching can reduce asymptotic efficiency relative to a regression framework, Angrist and Hahn (2004) show that there can be efficiency gain in a finite sample, even if there are no asymptotic efficiency gains.”
The Value of Knowing the Propensity Score for Estimating Average Treatment Effects (2016, background): “…score thus allows working with an “over-smoothed” nonparametric estimate of the conditional expectation function that converges very slowly, which alleviates concerns 3 about the “curse of dimensionality” in settings with many covariates (cf. Robins and Ritov, 1997; Angrist and Hahn, 2004).”
Lasting or Latent Scars? Swedish Evidence on the Long‐Term Effects of Job Displacement (2006, background): “12 Angrist and Hahn (2004) provide evidence on when propensity-score matching may dominate covariate matching.”
Assessing the Performance of Matching Algorithms When Selection into Treatment is Strong (2007, background): “First, for instance, Angrist and Hahn (2004) study efficiency comparisons of covariate matching with propensity score matching and show that the former may be more efficient in finite samples than the latter.”
Evaluating Anti-Poverty Programs (2005, background): “Under the independence assumption, exact matching on P(Z) eliminates selection bias, although it is not necessarily the most efficient impact estimator (Hahn, 1998; Angrist and Hahn, 2004).”