VIOLATING IGNORABILITY OF TREATMENT BY CONTROLLING FOR TOO MANY FACTORS

By Jeffrey M. Wooldridge. Econometric Theory, 2005

98 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/dcc21fb109d900894bfc2e230d81d7e2096a799e

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Citations in the corpus

Difference-in-Differences with multiple time periods (2018, background): “…level of aggregation than the covariates Xi; otherwise, one cannot separately identity the effect of ci and Xi on Yit. our proposal differs from Wooldridge (2005), Chernozhukov et al. (2013), Borusyak and Jaravel (2017), S loczyński (2017), de Chaisemartin and D’Haultfœuille (2018),…”
Should Instrumental Variables be Used as Matching Variables (2016, methodology): “Wooldridge (2005) formally shows that if treatment is randomized with”
A GENERAL DOUBLE ROBUSTNESS RESULT FOR ESTIMATING AVERAGE TREATMENT EFFECTS (2014, background): “If D is an indicator representing “more schooling,” we would not want to include D in X , as that would generally cause unconfoundedness to be violated—see, for example, Wooldridge (2005).”
Analysis of the bias of Matching and Difference-in-Difference under alternative earnings and selection processes (2015, background): “Wooldridge (2005) shows that controlling for variables altered by the treatment generates bias.”
The Impact of Improved Maize Varieties on Poverty in Mexico: A Propensity Score-Matching Approach (2010, background): “As pointed out by Wooldridge (2005), including too many factors in”
How Conditioning on Posttreatment Variables Can Ruin Your Experiment and What to Do about It (2018, background): “…dangers of post-treatment bias have long been recognized in the fields of statistics, econometrics, and political methodology (e.g., Rosenbaum 1984; Wooldridge 2005; King and Zeng 2006; Elwert and Winship 2014; Acharya, Blackwell, and Sen 2016), there is still significant confusion in the wider…”