Cause-and-effect relationships play a central role in how we perceive and make sense of
the world around us, how we act upon it, and ultimately, how we under stand ourselves …

The seven tools of causal inference, with reflections on machine learning Page 1 54
COMMUNICATIONS OF THE ACM | MARCH 2019 | VOL. 62 | NO. 3 contributed articles ILL US …

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

Path models to test claims about mediation and moderation are a staple of psychology. But
applied researchers may sometimes not understand the underlying causal inference …

We review concepts, principles, and tools that unify current approaches to causal analysis
and attend to new challenges presented by big data. In particular, we address the problem …

Current machine learning systems operate, almost exclusively, in a statistical, or model-free
mode, which entails severe theoretical limits on their power and performance. Such systems …

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

CAUSAL INFERENCE IN STATISTICS A Primer Causality is central to the understanding
and use of data. Without an understanding of cause–effect relationships, we cannot use data …

Some of the most prominent results in causal inference have been developed in the context
of atomic interventions, following the semantics of the do-operator and the inferential power …

Generalizing empirical findings to new environments, settings, or populations is essential in
most scientific explorations. This article treats a particular problem of generalizability, called …