Approximate Residual Balancing: De-Biased Inference of Average Treatment Effects in High Dimensions

25 Apr 2016 Susan Athey Guido W. Imbens Stefan Wager

There are many settings where researchers are interested in estimating average treatment effects and are willing to rely on the unconfoundedness assumption, which requires that the treatment assignment be as good as random conditional on pre-treatment variables. The unconfoundedness assumption is often more plausible if a large number of pre-treatment variables are included in the analysis, but this can worsen the performance of standard approaches to treatment effect estimation... (read more)

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  • METHODOLOGY
  • ECONOMETRICS
  • STATISTICS THEORY
  • STATISTICS THEORY