Results 121 to 130 of about 18,931,441 (264)

Integrating Copula-Based Random Forest and Deep Learning Approaches for Analyzing Heterogeneous Treatment Effects in Survival Analysis

Mathematics
This paper presents deep learning models—specifically, Long Short-Term Memory (LSTM) networks and hybrid Convolutional Neural Network–LSTM (CNN-LSTM) with a Copula-Based Random Forest (CBRF) model to estimate Heterogeneous Treatment Effects (HTEs) in ...
Jong-Min Kim
doaj   +1 more source

Continual Learning for Multimodal Data Fusion of a Soft Gripper

Advanced Robotics Research, EarlyView.
Models trained on a single data modality often struggle to generalize when exposed to a different modality. This work introduces a continual learning algorithm capable of incrementally learning different data modalities by leveraging both class‐incremental and domain‐incremental learning scenarios in an artificial environment where labeled data is ...
Nilay Kushawaha, Egidio Falotico
wiley   +1 more source

Dealing with Limited Overlap in Estimation of Average Treatment Effects [PDF]

Estimation of average treatment effects under unconfounded or ignorable treatment assignment is often hampered by lack of overlap in the covariate distributions.
Richard K. Crump, Oscar A. Mitnik, V. Joseph Hotz, Guido W. Imbens   +3 more
core  

Doubly weighted M-estimation for nonrandom assignment and missing outcomes

Journal of Causal Inference
This article proposes a class of M-estimators that double weight for the joint problems of nonrandom treatment assignment and missing outcomes. Identification of the main parameter of interest is achieved under unconfoundedness and missing at random ...
Negi Akanksha
doaj   +1 more source

Generalizing Conditional Average Treatment Effects From Nested Randomized Trials to Trial‐Eligible Individuals

Statistics in Medicine
Randomized controlled trials often enroll participants whose characteristics differ from those of a target population. When the distribution of effect modifiers differs between trial participants and the target population, treatment effects estimated ...
Lan Wen, I. Dahabreh, Yu-Han Chiu
semanticscholar   +1 more source

LLM‐Integrated Human–Robot Interaction System for Microrobots

Advanced Robotics Research, EarlyView.
This paper proposes an LLM‐based control framework for guiding microrobots using human natural language. This framework can convert the natural human speech into safe and executable command sets for reliable navigation in complex environments. The experimental results show high accuracy and robustness in task performance, demonstrating the potential of
Bairong Zhu, Amar Salehi, Tingting Yu
wiley   +1 more source

Cross‐Scale Hierarchical Targeted Delivery System Based on Small‐Scale Magnetic Robots

Advanced Robotics Research, EarlyView.
This article reviews a cross‐scale hierarchical targeted delivery system that integrates magnetic continuum robots and magnetic microrobots. By combining rapid long‐range navigation with precise microscale targeting, the system overcomes key limitations of single‐scale approaches.
Junjian Zhou, Zhiqiang Chen, Yuanbiao Ma, Zhiqiang Tang, Qianqian Wang   +4 more
wiley   +1 more source

Estimations of the Local Conditional Tail Average Treatment Effect [PDF]

The conditional tail average treatment effect (CTATE) is defined as a difference between the conditional tail expectations of potential outcomes, which can capture heterogeneity and deliver aggregated local information on treatment effects over different
Chen, Le-Yu, Yen, Yu-Min
core  

Adaptive Experimental Design Using the Propensity Score [PDF]

Many social experiments are run in multiple waves, or are replications of earlier social experiments. In principle, the sampling design can be modified in later stages or replications to allow for more efficient estimation of causal effects.
Keisuke Hirano, Jinyong Hahn, Dean Karlan   +2 more
core   +2 more sources

Model-based standardization using multiple imputation

BMC Medical Research Methodology
Background When studying the association between treatment and a clinical outcome, a parametric multivariable model of the conditional outcome expectation is often used to adjust for covariates.
Antonio Remiro-Azócar, Anna Heath, Gianluca Baio   +2 more
doaj   +1 more source