1 citations · 3 across the 14 of their papers we have counts for
18 papers
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…
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…
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…
Fair Policy Learning under Bipartite Network Interference: Learning Fair and Cost-Effective Environmental Policies
Raphael C. Kim, Rachel C. Nethery, Kevin L. Chen +1
Numerous studies have shown the harmful effects of airborne pollutants on human health. Vulnerable groups and communities often bear a disproportionately larger health burden due t…
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…
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…