Revisiting Randomization with the Cube Method
We propose a novel randomization approach for randomized controlled trials (RCTs), based on the cube method developed by Deville and Till\'e (2004). The cube method allows for the selection of balanced samples across various covariate types, ensuring consistent adherence to balance tests and, whence, substantial precision gains when estimating treatment effects. We establish several statistical properties for the population and sample average treatment effects under randomization using the cube method. We formally derive and compare bounds on imbalances depending on the number of units $n$ and the number of covariates $p$ considered for the balancing. We show that our randomization approach outperforms methods proposed in the literature when $p$ is large and $p/n$ tends to 0. We run simulation studies to illustrate the substantial gains from the cube method for a large set of covariates.
Code (0)
등록된 구현이 없습니다.
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Solving Rubik's Cube with a Robot Hand
We demonstrate that models trained only in simulation can be used to solve a manipulation problem of unprecedented complexity on a real robot. This is made possible by two key components: a novel algorithm, which we call…
Meta-LearningRubik's CubeState EstimationRobust Visual Sim-to-Real Transfer for Robotic Manipulation
Learning visuomotor policies in simulation is much safer and cheaper than in the real world. However, due to discrepancies between the simulated and real data, simulator-trained policies often fail when transferred to re…
object-detectionObject DetectionPose EstimationRevisiting Rubik's Cube: Self-supervised Learning with Volume-wise Transformation for 3D Medical Image Segmentation
Deep learning highly relies on the quantity of annotated data. However, the annotations for 3D volumetric medical data require experienced physicians to spend hours or even days for investigation. Self-supervised learnin…
Image SegmentationMedical Image SegmentationPancreas SegmentationRubik's Cube+2Transferring Dexterous Manipulation from GPU Simulation to a Remote Real-World TriFinger
We present a system for learning a challenging dexterous manipulation task involving moving a cube to an arbitrary 6-DoF pose with only 3-fingers trained with NVIDIA's IsaacGym simulator. We show empirical benefits, both…
GPUPositionTowards Learning Rubik's Cube with N-tuple-based Reinforcement Learning
This work describes in detail how to learn and solve the Rubik's cube game (or puzzle) in the General Board Game (GBG) learning and playing framework. We cover the cube sizes 2x2x2 and 3x3x3. We describe in detail the cu…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Rubik's Cube