Details

Autor(en) / Beteiligte

• Farrell, Max H.

Titel

Robust inference on average treatment effects with possibly more covariates than observations

Ist Teil von

• Journal of econometrics, 2015-11, Vol.189 (1), p.1-23

Ort / Verlag

Amsterdam: Elsevier B.V

Erscheinungsjahr

2015

Link zum Volltext

Quelle

Elsevier ScienceDirect Journals Complete

Beschreibungen/Notizen

• This paper concerns robust inference on average treatment effects following model selection. Under selection on observables, we construct confidence intervals using a doubly-robust estimator that are robust to model selection errors and prove their uniform validity over a large class of models that allows for multivalued treatments with heterogeneous effects and selection amongst (possibly) more covariates than observations. The semiparametric efficiency bound is attained under appropriate conditions. Precise conditions are given for any model selector to yield these results, and we specifically propose the group lasso, which is apt for treatment effects, and derive new results for high-dimensional, sparse multinomial logistic regression. Both a simulation study and revisiting the National Supported Work demonstration show our estimator performs well in finite samples.