Causal Inference Quotes

“Fighting for the acceptance of Bayesian networks in AI was a picnic compared with the fight I had to wage for causal diagrams [in the stormy waters of statistics].”
― Judea Pearl, The Book of Why: The New Science of Cause and Effect

“Pearl combines aspects of structural equations models and path diagrams. In this approach, assumptions underlying causal statements are coded as missing links in the path diagrams. Mathematical methods are then used to infer, from these path diagrams, which causal effects can be inferred from the data, and which cannot. Pearl's work is interesting, and many researchers find his arguments that path diagrams are a natural and convenient way to express assumptions about causal structures appealing. In our own work, perhaps influenced by the type of examples arising in social and medical sciences, we have not found this approach to aid drawing of causal inferences, and we do not discuss it further in this text.”
― Guido W. Imbens, Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction

“To me AI becomes interesting when it has awareness of causality, not just correlation. When it can deal with ambiguity and that sensitive dependencies exist outside the bounds of a defined data set which can lead to different outcomes. - Tom Golway”
― Tom Golway