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 long-term effects of novel treatments
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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.