Hello,
I am estimating a treatment using differences-in-differences. The subjects in my treatment sample were allowed to drop out, which probably induced a bias in my estimate. I've been considering different methods for estimating a treatment effect on the treated, including LATE/CACE, but am having a difficult time finding how to apply these methods in a DiD framework.
Do you think it would be plausible to impute the "missing" observations after the treatment subjects dropped? The assumption would be that the subjects that dropped continued on the same trend they were on, and this would allow me to retain a full sample.
This wouldn't be a perfect estimate, of course, because it's possible that the subjects don't continue on their same path...and I worry that the variability of imputed values would be unrealistically small.
Any comments would be appreciated.
Thanks!
Related Posts with DiD - sample attrition, what if I impute missing data?
Difference in Difference with multiple treatment periods and multiple treatmentsGood day all! This is my first post on Stata list. Please let me know if any additional information …
Regression without estimating the standard errorsDear All: I am currently estimating a series of regressions with fixed effects. However, the standa…
[HELP] Appending several CSV files togetherHello. I am attempting to append several .csv files (about 44) all from one folder. The files come f…
Is there a way to tsset aggregated binomial data?Hi, I am wanting to do a segmented regression (as an interrupted time series) using binary outcome …
Optimize by minimizing the value of an equation to get a parameter value for each observationDear Everyone, Software Used: STATA 15.1 I am trying to get a parameter value (R) for each observa…
Subscribe to:
Post Comments (Atom)
0 Response to DiD - sample attrition, what if I impute missing data?
Post a Comment