Derives the efficient influence function and doubly robust estimators for the local average treatment effect on the treated in instrumented DiD designs with staggered exposure and covariates.
van der Laan and Sherri Rose.Targeted Learning: Causal Inference for Observational and Experimental Data
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Loss-weighted targeting in TMLE introduces more systematic bias than clever-covariate-scaled targeting under positivity stress, while a proposed Lepski-type adaptive truncation with brake improves stability over fixed rules like c/(sqrt(n) log n) with c=5 or 6.
citing papers explorer
-
Doubly Robust Instrumented Difference-in-Differences
Derives the efficient influence function and doubly robust estimators for the local average treatment effect on the treated in instrumented DiD designs with staggered exposure and covariates.
-
Investigating Targeting Strategies and Truncation in TMLE for the Average Treatment Effect under Practical Positivity Violations
Loss-weighted targeting in TMLE introduces more systematic bias than clever-covariate-scaled targeting under positivity stress, while a proposed Lepski-type adaptive truncation with brake improves stability over fixed rules like c/(sqrt(n) log n) with c=5 or 6.