The Q-Learner decomposes ratio CATE into odds ratios for propensity-based estimation and introduces doubly robust meta-learners that perform well on RCT and observational datasets.
Optimal doubly robust estimation of heterogeneous causal effects
10 Pith papers cite this work. Polarity classification is still indexing.
10
Pith papers citing it
representative citing papers
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.
A doubly robust calibration-plus-control-variate estimator allows causal inference with multiple misclassified binary exposures and shows throat-swab data understate P. aeruginosa's effect on lung function in cystic fibrosis.
CausalGuard aggregates LLM-proposed and data-pruned DAGs to weight doubly robust pseudo-outcomes and applies conformal calibration to deliver finite-sample marginal coverage for conditional average treatment effects under graph uncertainty.
Matrix-weighted regularization for robust multi-task regression achieves optimal MSE under weaker spectral assumptions and performs no worse than independent learning when balancedness is poor.
Bayesian X-Learner delivers calibrated posterior inference for CATE by combining cross-fitted doubly robust pseudo-outcomes with a Welsch redescending pseudo-likelihood and MCMC sampling.
DSL uses doubly robust pseudo-outcomes and a multi-output neural network to jointly estimate time-varying conditional average treatment effects for right-censored survival data.
Introduces partial identification bounds and a double-robust SurvB-learner meta-learner for estimating robust CATE in survival analysis under informative censoring.
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.
A semiparametric method using conditional influence functions and local linear regression is proposed to estimate CATE functions in the target population from nested trial data.
citing papers explorer
-
Beyond Differences: Doubly Robust Meta-Learners for Ratio-Based Treatment Effects
The Q-Learner decomposes ratio CATE into odds ratios for propensity-based estimation and introduces doubly robust meta-learners that perform well on RCT and observational datasets.
-
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.
-
Causal Inference with Multiple Misclassified Exposures: A Control Variate-Adjusted Calibration Weighting Approach
A doubly robust calibration-plus-control-variate estimator allows causal inference with multiple misclassified binary exposures and shows throat-swab data understate P. aeruginosa's effect on lung function in cystic fibrosis.
-
CausalGuard: Conformal Inference under Graph Uncertainty
CausalGuard aggregates LLM-proposed and data-pruned DAGs to weight doubly robust pseudo-outcomes and applies conformal calibration to deliver finite-sample marginal coverage for conditional average treatment effects under graph uncertainty.
-
Multi-task Linear Regression without Eigenvalue Lower Bounds: Adaptivity, Robustness, and Safety
Matrix-weighted regularization for robust multi-task regression achieves optimal MSE under weaker spectral assumptions and performs no worse than independent learning when balancedness is poor.
-
Bayesian X-Learner: Calibrated Posterior Inference for Heterogeneous Treatment Effects under Heavy-Tailed Outcomes
Bayesian X-Learner delivers calibrated posterior inference for CATE by combining cross-fitted doubly robust pseudo-outcomes with a Welsch redescending pseudo-likelihood and MCMC sampling.
-
Estimating heterogeneous treatment effects with survival outcomes via a deep survival learner
DSL uses doubly robust pseudo-outcomes and a multi-output neural network to jointly estimate time-varying conditional average treatment effects for right-censored survival data.
-
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.
-
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.
-
Generalizing conditional average treatment effects from nested randomized trials to all trial-eligible individuals
A semiparametric method using conditional influence functions and local linear regression is proposed to estimate CATE functions in the target population from nested trial data.