Causal inference from observational data is crucial for many disciplines such as medicine
and economics. However, sharp bounds for causal effects under relaxations of the …

Scientific hypotheses typically concern specific aspects of complex, imperfectly understood
or entirely unknown mechanisms, such as the effect of gene expression levels on …

Unobserved confounding is common in many applications, making causal inference from
observational data challenging. As a remedy, causal sensitivity analysis is an important tool …

Estimating causal quantities from observational data is crucial for understanding the safety
and effectiveness of medical treatments. However, to make reliable inferences, medical …

This paper provides a comprehensive review of deep structural causal models (DSCMs),
particularly focusing on their ability to answer counterfactual queries using observational …

Counterfactual inference aims to estimate the counterfactual outcome at the individual level
given knowledge of an observed treatment and the factual outcome, with broad applications …

Recent progress in Neural Causal Models (NCMs) showcased how identification and partial
identification of causal effects can be automatically carried out via training of neural …

We propose an importance sampling method for tractable and efficient estimation of
counterfactual expressions in general settings, named Exogenous Matching. By minimizing …

One of the main concerns while deploying machine learning models in real-world
applications is fairness. Counterfactual fairness has emerged as an intuitive and natural …

Causality is widely used in fairness analysis to prevent discrimination on sensitive attributes,
such as genders in career recruitment and races in crime prediction. However, the current …