4 citations · 4 across the 3 of their papers we have counts for
5 papers
Deconfounding Scores and Representation Learning for Causal Effect Estimation with Weak Overlap
Oscar Clivio, Alexander D'Amour, Alexander Franks +3
Overlap, also known as positivity, is a key condition for causal treatment effect estimation. Many popular estimators suffer from high variance and become brittle when features dif…
Towards Representation Learning for Weighting Problems in Design-Based Causal Inference
Oscar Clivio, Avi Feller, Chris Holmes
Reweighting a distribution to minimize a distance to a target distribution is a powerful and flexible strategy for estimating a wide range of causal effects, but can be challenging…
Neural Score Matching for High-Dimensional Causal Inference
Oscar Clivio, Fabian Falck, Brieuc Lehmann +2
Traditional methods for matching in causal inference are impractical for high-dimensional datasets. They suffer from the curse of dimensionality: exact matching and coarsened exact…
Learning to Defer for Causal Discovery with Imperfect Experts
Oscar Clivio, Divyat Mahajan, Perouz Taslakian +4
Integrating expert knowledge, e.g. from large language models, into causal discovery algorithms can be challenging when the knowledge is not guaranteed to be correct. Expert recomm…
A Critical Review of Causal Reasoning Benchmarks for Large Language Models
Linying Yang, Vik Shirvaikar, Oscar Clivio +1
Numerous benchmarks aim to evaluate the capabilities of Large Language Models (LLMs) for causal inference and reasoning. However, many of them can likely be solved through the retr…