DSIV-CFR learns instrumental variables from observed covariates and uses a generalized method of moments to estimate sequential treatment effects under unmeasured confounding, but the identification proof is incomplete and the experiments measure only factual prediction error.
Estimating the effect of training programs on earnings
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Sequential Treatment Effect Estimation with Unmeasured Confounders
DSIV-CFR learns instrumental variables from observed covariates and uses a generalized method of moments to estimate sequential treatment effects under unmeasured confounding, but the identification proof is incomplete and the experiments measure only factual prediction error.