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
4 Pith papers cite this work. Polarity classification is still indexing.
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Introduces BRIDGE and SKFM algorithms that detect latent confounders via non-closing Lie brackets in interventional vector fields derived from density ratios.
The paper develops set-valued policies and conformal policy learning methods that output treatment sets with marginal coverage guarantees for robust decision-making under uncertainty.
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
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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.
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Latent Confounded Causal Discovery via Lie Bracket Geometry
Introduces BRIDGE and SKFM algorithms that detect latent confounders via non-closing Lie brackets in interventional vector fields derived from density ratios.
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Set-Valued Policy Learning
The paper develops set-valued policies and conformal policy learning methods that output treatment sets with marginal coverage guarantees for robust decision-making under uncertainty.
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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.