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

Max Farrell

Journal of Econometrics, 2015, vol. 189, issue 1, 1-23

Abstract: 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.

Keywords: High-dimensional sparse model; Heterogeneous treatment effects; Uniform inference; Model selection; Doubly-robust estimator; Unconfoundedness; Group lasso (search for similar items in EconPapers)
JEL-codes: C21 C31 C52 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (164)

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Related works:
Working Paper: Robust Inference on Average Treatment Effects with Possibly More Covariates than Observations (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:189:y:2015:i:1:p:1-23

DOI: 10.1016/j.jeconom.2015.06.017

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