High-dimensional regression adjustments in randomized experiments.

  title={High-dimensional regression adjustments in randomized experiments.},
  author={Stefan Wager and Wenfei Du and Jonathan E. Taylor and Robert Tibshirani},
  journal={Proceedings of the National Academy of Sciences of the United States of America},
  volume={113 45},
We study the problem of treatment effect estimation in randomized experiments with high-dimensional covariate information and show that essentially any risk-consistent regression adjustment can be used to obtain efficient estimates of the average treatment effect. Our results considerably extend the range of settings where high-dimensional regression adjustments are guaranteed to provide valid inference about the population average treatment effect. We then propose cross-estimation, a simple… CONTINUE READING
Recent Discussions
This paper has been referenced on Twitter 3 times over the past 90 days. VIEW TWEETS

From This Paper

Figures, tables, and topics from this paper.


Publications referenced by this paper.
Showing 1-10 of 46 references

Imbens G (2016) The econometrics of randomized experiments

  • S Athey
  • 2016
Highly Influential
3 Excerpts

Causal inference with random forests

  • S Wager
  • PhD thesis (Stanford University,
  • 2016

Efficient inference of average treatment effects in high dimensions via approximate residual balancing

  • S Athey, GW Imbens, S Wager
  • 2016

A (2015) De-biasing the lasso: Optimal sample size for Gaussian designs. arXiv:1508.02757

  • A Javanmard, Montanari
  • 2015

Similar Papers

Loading similar papers…