This review presents empirical researchers with recent advances in causal inference, and
stresses the paradigmatic shifts that must be undertaken in moving from traditional statistical …

This paper summarizes recent advances in causal inference and underscores the
paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to …

A Turing Award-winning computer scientist and statistician shows how understanding
causality has revolutionized science and will revolutionize artificial intelligence" Correlation …

Written by one of the preeminent researchers in the field, this book provides a
comprehensive exposition of modern analysis of causation. It shows how causality has …

Algorithmic systems that employ machine learning play an increasing role in making
substantive decisions in modern society, ranging from online personalization to insurance …

We propose a new definition of actual causes, using structural equations to model
counterfactuals. We show that the definition yields a plausible and elegant account of …

Recent work has discussed the limitations of counterfactual explanations to recommend
actions for algorithmic recourse, and argued for the need of taking causal relationships …

There has been a recent resurgence of interest in explainable artificial intelligence (XAI) that
aims to reduce the opaqueness of AI-based decision-making systems, allowing humans to …

This paper investigates the problem of bounding counterfactual queries from an arbitrary
collection of observational and experimental distributions and qualitative knowledge about …

The supplementary material contains details on treatment effect estimation performed
separately on RCT data (Section A) and on observational data (Section B), derivations of the …