activity
20222024
most citedBounding Counterfactuals under Selection Bias

1 citations · 2 across the 6 of their papers we have counts for

collaborators

6 papers

cs.GT2024

SACRÉ BLEU: Self-Assessed Creator Royalties Énforced by Balancing Liquidity Estimation & Utility (A formal definition and analysis of Ethereum Request for Comment ERC-7526)

David Huber, Arran Schlosberg

The secondary market for Ethereum non-fungible tokens (NFTs) has resulted in over $1.8bn being paid to creators in the form of a sales tax commonly called creator royalties. This w…

cs.AI2023★ 1 cited

Tractable Bounding of Counterfactual Queries by Knowledge Compilation

David Huber, Yizuo Chen, Alessandro Antonucci +2

We discuss the problem of bounding partially identifiable queries, such as counterfactuals, in Pearlian structural causal models. A recently proposed iterated EM scheme yields an i…

stat.ME2023

Approximating Counterfactual Bounds while Fusing Observational, Biased and Randomised Data Sources

Marco Zaffalon, Alessandro Antonucci, Rafael Cabañas +1

We address the problem of integrating data from multiple, possibly biased, observational and interventional studies, to eventually compute counterfactuals in structural causal mode…

cs.AI2023

Efficient Computation of Counterfactual Bounds

Marco Zaffalon, Alessandro Antonucci, Rafael Cabañas +2

We assume to be given structural equations over discrete variables inducing a directed acyclic graph, namely, a structural causal model, together with data about its internal nodes…

cs.AI2022

Learning to Bound Counterfactual Inference from Observational, Biased and Randomised Data

Marco Zaffalon, Alessandro Antonucci, David Huber +1

We address the problem of integrating data from multiple, possibly biased, observational and interventional studies, to eventually compute counterfactuals in structural causal mode…

stat.ML2022★ 1 cited

Bounding Counterfactuals under Selection Bias

Marco Zaffalon, Alessandro Antonucci, Rafael Cabañas +2

Causal analysis may be affected by selection bias, which is defined as the systematic exclusion of data from a certain subpopulation. Previous work in this area focused on the deri…