8 papers
Causal Stability Selection
Falco J. Bargagli-Stoffi, Omar Melikechi
Identifying covariates that modify treatment effects is a central problem in causal inference. Yet existing data-adaptive procedures do not provide finite-sample control over the e…
From Tokens to Policy: Causal and Interpretable Heterogeneous Treatment Effects Identification
Riccardo Cadei, Frank Otchere, Nyasha Tirivayi +3
Heterogeneous Treatment Effect (HTE) identification is crucial to explain the impact of an intervention and optimize our policies accordingly. Existing approaches trade expressivit…
Beyond Explainable AI (XAI): An Overdue Paradigm Shift and Post-XAI Research Directions
Saleh Afroogh, Syed Ishtiaque Ahmed, Petra Ahrweiler +46
This study provides a cross-disciplinary examination of Explainable Artificial Intelligence (XAI) approaches-focusing on deep neural networks (DNNs) and large language models (LLMs…
Transporting Predictions via Double Machine Learning: Predicting Partially Unobserved Students' Outcomes
Falco J. Bargagli-Stoffi, Emma Landry, Kevin P. Josey +3
Educational policymakers often lack data on student outcomes where standardized tests were not administered. Machine learning can predict unobserved outcomes in target populations…
Bayesian Nonparametrics for Principal Stratification with Continuous Post-Treatment Variables
Dafne Zorzetto, Antonio Canale, Fabrizia Mealli +2
Principal stratification provides a causal inference framework for investigating treatment effects in the presence of a post-treatment variable. Principal strata play a key role in…
Modern Causal Inference Approaches to Improve Power for Subgroup Analysis in Randomized Controlled Trials
Antonio D'Alessandro, Jiyu Kim, Samrachana Adhikari +3
Randomized controlled trials (RCTs) often include subgroup analyses to assess whether treatment effects vary across pre-specified patient populations. However, these analyses frequ…