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arXiv; 2023.http://arxiv.org/abs/2011.08047, arXiv:2011.08047 [stat]

Causal inference methods for combining randomized trials, observational studies: a review , author= · 2011 · arXiv 2011.08047

4 Pith papers cite this work. Polarity classification is still indexing.

4 Pith papers citing it
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fields

cs.AI 1 cs.LG 1 math.ST 1 stat.ME 1

years

2026 4

verdicts

UNVERDICTED 3 CONDITIONAL 1

representative citing papers

Private Rate-Double-Robust Inference

math.ST · 2026-06-18 · unverdicted · novelty 8.0

Local privacy mechanisms preserve rate-double-robustness, enabling unbiased and semiparametrically efficient inference on target parameters indexed linearly by infinite-dimensional and nonlinearly by low-dimensional components from noisy private data.

An Introduction to Causal Reinforcement Learning

cs.AI · 2026-06-23 · unverdicted · novelty 5.0

Proposes causal reinforcement learning (CRL) as a framework that decomposes RL environments into structural causal models to unify online, off-policy, and causal learning while defining new tasks including generalized policy learning and counterfactual learning.

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Showing 4 of 4 citing papers.

  • Private Rate-Double-Robust Inference math.ST · 2026-06-18 · unverdicted · none · ref 69

    Local privacy mechanisms preserve rate-double-robustness, enabling unbiased and semiparametrically efficient inference on target parameters indexed linearly by infinite-dimensional and nonlinearly by low-dimensional components from noisy private data.

  • Real vs. Semi-Simulated: Rethinking Evaluation for Treatment Effect Estimation cs.LG · 2026-05-11 · unverdicted · none · ref 49

    Large-scale study finds that counterfactual metrics on semi-simulated data do not select the same estimators as observable metrics on real data, and benchmark rankings fail to transfer.

  • An Introduction to Causal Reinforcement Learning cs.AI · 2026-06-23 · unverdicted · none · ref 28

    Proposes causal reinforcement learning (CRL) as a framework that decomposes RL environments into structural causal models to unify online, off-policy, and causal learning while defining new tasks including generalized policy learning and counterfactual learning.

  • Longitudinal Outcomes Truncated by Death: Causal Estimands and Bayesian Estimators stat.ME · 2026-04-29 · conditional · none · ref 16

    A review and simulation study recommending SACE plus RMST as the most interpretable pair of estimands for longitudinal outcomes truncated by death.