Tootfinder

Generalizable estimation of conditional average treatment effects using Causal Forest in randomized
Rikuta Hamaya, Etsuji Suzuki, Konan Hara
https://arxiv.org/abs/2506.12296

Generalizable estimation of conditional average treatment effects using Causal Forest in randomized
Generalizing conditional average treatment effects (CATE) estimates in a randomized controlled trial (RCT) to a broader source population can be challenging because of selection bias and high-dimensional covariates. We aim to evaluate CATE estimation approaches using Causal Forest that address selection bias due to trial participation. We propose and compare four CATE estimation approaches using Causal Forest: (1) ignoring selection variables, (2) including selection variables, (3) using invers…

ONION: A Multi-Layered Framework for Participatory ER Design
Viktoriia Makovska, George Fletcher, Julia Stoyanovich
https://arxiv.org/abs/2507.08702 https:…

ONION: A Multi-Layered Framework for Participatory ER Design
We present ONION, a multi-layered framework for participatory Entity-Relationship (ER) modeling that integrates insights from design justice, participatory AI, and conceptual modeling. ONION introduces a five-stage methodology: Observe, Nurture, Integrate, Optimize, Normalize. It supports progressive abstraction from unstructured stakeholder input to structured ER diagrams. Our approach aims to reduce designer bias, promote inclusive participation, and increase transparency through the modeli…