Introduces a cross-fitted orthogonal hypergradient estimator derived from the efficient influence function that achieves asymptotic normality and uniform control for bilevel gradient estimation under quadratic losses.
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Semiparametric doubly robust targeted double machine learning: a review
17 Pith papers cite this work. Polarity classification is still indexing.
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Risk-controlled post-processing yields a threshold-structured policy that follows the baseline except where an oracle fallback sharply reduces conditional violation risk, achieving O(log n/n) expected excess risk in i.i.d. settings and exact risk control under exchangeability.
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.
Targeted synthetic control (TSC) is a new two-stage estimator that applies a one-dimensional weight-tilting update to debias synthetic control weights and guarantees the final counterfactual is a convex combination of control outcomes.
Develops Grenander-type and debiased machine learning estimators for the sublevel-set probability curve of the CATE function, shown to be non-pathwise differentiable, along with its piecewise linear approximation.
A neural doubly robust proxy causal learning framework using mean embeddings for treatment bridges provides consistent estimators for causal dose-response functions under unobserved confounding for continuous and structured treatments.
CIVeX maps agent tool calls to structural causal queries, checks identifiability, and issues auditable verdicts to prevent false executions while preserving utility on confounded benchmarks.
A semi-supervised kernel two-sample test integrates unlabeled covariate data to achieve asymptotic normality under the null, higher power than standard kernel tests, and consistency against fixed and local alternatives.
DOPE is a Neyman-orthogonal one-step semiparametric estimator that removes first-order bias in functional estimates from neural operators by learning weights via Riesz regression.
Conformal inference produces robust prediction intervals for treatment effects under experimental attrition, outperforming complete-case, imputation, and weighting approaches in simulations.
Introduces partial identification bounds and a double-robust SurvB-learner meta-learner for estimating robust CATE in survival analysis under informative censoring.
Unified targeted regularization framework for causal effect estimation with EDF outcomes using neural networks that jointly estimate outcome model, propensity scores, and fluctuation parameter.
Develops novel one-step and TMLE estimators for ATE and ATT under front-door assumptions with ML nuisance estimation, root-n consistency proofs, and doubly robust tests for identification assumptions.
Develops m-th order estimators for dose-response functions based on higher-order influence functions that attain the fastest known convergence rates under stated conditions.
Develops improved Fréchet-Hoeffding-style bounds and nonparametric estimators for the fraction negatively affected (FNA) by treatment, using Pearson correlation between potential outcomes as a sensitivity parameter.
The crumble package provides nonparametric tools for estimating natural direct/indirect effects, randomized interventional effects, and recanting-twin effects in mediation analysis, with guidance on identification assumptions and non-binary treatments illustrated via case studies.
citing papers explorer
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Semiparametric Efficient Bilevel Gradient Estimation
Introduces a cross-fitted orthogonal hypergradient estimator derived from the efficient influence function that achieves asymptotic normality and uniform control for bilevel gradient estimation under quadratic losses.
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Risk-Controlled Post-Processing of Decision Policies
Risk-controlled post-processing yields a threshold-structured policy that follows the baseline except where an oracle fallback sharply reduces conditional violation risk, achieving O(log n/n) expected excess risk in i.i.d. settings and exact risk control under exchangeability.
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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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Targeted Synthetic Control Method
Targeted synthetic control (TSC) is a new two-stage estimator that applies a one-dimensional weight-tilting update to debias synthetic control weights and guarantees the final counterfactual is a convex combination of control outcomes.
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Nonparametric inference for sublevel-set probabilities of conditional average treatment effect functions
Develops Grenander-type and debiased machine learning estimators for the sublevel-set probability curve of the CATE function, shown to be non-pathwise differentiable, along with its piecewise linear approximation.
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Doubly Robust Proxy Causal Learning with Neural Mean Embeddings
A neural doubly robust proxy causal learning framework using mean embeddings for treatment bridges provides consistent estimators for causal dose-response functions under unobserved confounding for continuous and structured treatments.
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CIVeX: Causal Intervention Verification for Language Agents
CIVeX maps agent tool calls to structural causal queries, checks identifiability, and issues auditable verdicts to prevent false executions while preserving utility on confounded benchmarks.
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A Semi-Supervised Kernel Two-Sample Test
A semi-supervised kernel two-sample test integrates unlabeled covariate data to achieve asymptotic normality under the null, higher power than standard kernel tests, and consistency against fixed and local alternatives.
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Debiased neural operators for estimating functionals
DOPE is a Neyman-orthogonal one-step semiparametric estimator that removes first-order bias in functional estimates from neural operators by learning weights via Riesz regression.
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Conformal Inference for Experimental Attrition in Social Science Research
Conformal inference produces robust prediction intervals for treatment effects under experimental attrition, outperforming complete-case, imputation, and weighting approaches in simulations.
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Assessing the robustness of heterogeneous treatment effects in survival analysis under informative censoring
Introduces partial identification bounds and a double-robust SurvB-learner meta-learner for estimating robust CATE in survival analysis under informative censoring.
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Targeted Regularization for Causal Effect Estimation with Exponential Dispersion Family Outcomes
Unified targeted regularization framework for causal effect estimation with EDF outcomes using neural networks that jointly estimate outcome model, propensity scores, and fluctuation parameter.
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Flexible Nonparametric Inference for Causal Effects under the Front-Door Model
Develops novel one-step and TMLE estimators for ATE and ATT under front-door assumptions with ML nuisance estimation, root-n consistency proofs, and doubly robust tests for identification assumptions.
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Fast convergence rates for dose-response estimation
Develops m-th order estimators for dose-response functions based on higher-order influence functions that attain the fastest known convergence rates under stated conditions.
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Quantifying Individual Risk for Binary Outcomes
Develops improved Fréchet-Hoeffding-style bounds and nonparametric estimators for the fraction negatively affected (FNA) by treatment, using Pearson correlation between potential outcomes as a sensitivity parameter.
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crumble: A comprehensive framework for modern causal mediation analysis with intermediate confounding
The crumble package provides nonparametric tools for estimating natural direct/indirect effects, randomized interventional effects, and recanting-twin effects in mediation analysis, with guidance on identification assumptions and non-binary treatments illustrated via case studies.
- Evaluating causal indirect effects when mediators are left-censored by assay limit of quantification