Sharpening bounds on principal effects with covariates.

  title={Sharpening bounds on principal effects with covariates.},
  author={Dustin M. Long and Michael G. Hudgens},
  volume={69 4},
Estimation of treatment effects in randomized studies is often hampered by possible selection bias induced by conditioning on or adjusting for a variable measured post-randomization. One approach to obviate such selection bias is to consider inference about treatment effects within principal strata, that is, principal effects. A challenge with this approach is that without strong assumptions principal effects are not identifiable from the observable data. In settings where such assumptions are… CONTINUE READING
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