Results 101 to 110 of about 18,931,441 (264)

Causal Clustering for Conditional Average Treatment Effects Estimation and Subgroup Discovery

2025 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI)
Estimating heterogeneous treatment effects is critical in domains such as personalized medicine, resource allocation, and policy evaluation. A central challenge lies in identifying subpopulations that respond differently to interventions, thereby enabling more targeted and effective decision-making.
Zilong Wang, Turgay Ayer, Shihao Yang
openaire   +3 more sources

Generative Artificial Intelligence Utilization and Perspectives Among Reproductive‐Age Women With Systemic Autoimmune and Rheumatic Diseases

Arthritis Care &Research, EarlyView.
Objective Reproductive‐age women with systemic autoimmune and rheumatic diseases (SARDs) have unique information needs related to their SARDs and reproductive health. We sought to understand their use of and receptivity to current and hypothetical generative artificial intelligence (AI) tools for health information‐seeking. Methods We conducted a cross‐
Mariam Arif, Catherine Wright, Samantha Rodriguez, Sarah Graves, Tamar Krishnamurti, Mehret Birru Talabi   +5 more
wiley   +1 more source

The Association of Gout Flare Resolution With Physical Activity and Sleep: A Prospective Remote‐Monitoring Study

Arthritis Care &Research, EarlyView.
Objective This study aimed to determine whether gout flare resolution is associated with physical activity and sleep. These findings were intended to inform the future development of a definition for flare resolution. Methods Data from 81 participants with gout who experienced at least one flare during a six‐month prospective observational study were ...
Sarah Stewart, Boston Girven, Mia Clarke, Irene Zeng, Angelo Gaffo, Lisa K. Stamp, N. Lawrence Edwards, David Rice, Tania Ka'ai, Karl Rudolph, Julie Collis, Nicola Kayes, Nicola Dalbeth   +12 more
wiley   +1 more source

Estimation of the Marginal Causal Risk Ratio in Survival Data Using Inverse Probability Treatment Weighting (IPTW)

مجله اپیدمیولوژی ایران, 2018
One of the traditional methods used for the analysis of survival data is the Cox regression technique. This method calculates the conditional risk ratio.
N, , , , H Poustchi, M Yaseri
doaj  

Estimating Conditional Average Treatment Effects via Sufficient Representation Learning

Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence
Estimating the conditional average treatment effects (CATE) is very important in causal inference and has a wide range of applications across many fields. In the estimation process of CATE, the unconfoundedness assumption is typically required to ensure the identifiability of the regression problems.
Pengfei Shi, Wei Zhong, Xinyu Zhang, Ningtao Wang, Xing Fu, Weiqiang Wang 0002, Yin Jin   +6 more
openaire   +3 more sources

dynoGP: Deep Gaussian Processes for Dynamic System Identification

International Journal of Adaptive Control and Signal Processing, EarlyView.
This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli, Marco Forgione, Dario Piga, Marco Zaffalon   +3 more
wiley   +1 more source

Optimizing NiMo Electrocatalyst Coatings for Hydrogen Evolution in Alkaline Electrolysis With The Machine Learning‐Guided Autonomous Platform CatBot

Advanced Functional Materials, EarlyView.
This work presents a closed‐loop optimization workflow integrating CatBot, an automated synthesis and testing system, to discover NiMo hydrogen‐evolution catalysts. CatBot synthesised and evaluated 228 unique samples, identifying the best performing catalysts for transfer to nickel‐foam electrodes. The best candidate is highly active, reaching 1 Acm−2${
Paolo Vincenzo Freiesleben de Blasio, Jonas Elsborg, Elif Coşkun, Andreas Burger, Pradipkumar Manubhai Leuaa, Thu Pham Anh Phan, Jin Hyun Chang, Christodoulos Chatzichristodoulou   +7 more
wiley   +1 more source

CATE meets ML -- The Conditional Average Treatment Effect and Machine Learning

, 2021
Tutorial paper, V2 contains fixes of minor ...
openaire   +3 more sources

Application of Causal Forest Model to Examine Treatment Effect Heterogeneity in Substance Use Disorder Psychosocial Treatments

International Journal of Methods in Psychiatric Research
Objectives Heterogeneity of treatment effect (HTE) is a concern in substance use disorder (SUD) treatments but has not been rigorously examined. This exploratory study applied a causal forest approach to examine HTE in psychosocial SUD treatments ...
Ryoko Susukida, Masoumeh Amin‐Esmaeili, Elena Badillo‐Goicoechea, Trang Q. Nguyen, Elizabeth A. Stuart, Michael Rosenblum, Kelly E. Dunn, Ramin Mojtabai   +7 more
doaj   +1 more source

rdhte: Conditional Average Treatment Effects in RD Designs

Understanding causal heterogeneous treatment effects based on pretreatment covariates is a crucial aspect of empirical work. Building on Calonico, Cattaneo, Farrell, Palomba, and Titiunik (2025), this article discusses the software package rdhte for estimation and inference of heterogeneous treatment effects in sharp regression discontinuity (RD ...
Calonico, Sebastian, Cattaneo, Matias D., Farrell, Max H., Palomba, Filippo, Titiunik, Rocio   +4 more
openaire   +2 more sources