The Seven Tools of Causal Inference with Reflections on Machine Learning

JUDEA PEARL, UCLA Computer Science Department, USA ACM Reference Format: Judea Pearl. 2018. The Seven Tools of Causal Inference with Reflections on Machine Learning. 1, 1 (November 2018), 6 pages. https://doi.org/10.1145/nnnnnnn.nnnnnnn The dramatic...

Causal vs. Statistical Inference

Why is correlation not enough, or is correlation enough? The question bugging the scientific community for a century. A machine learning view on the subject. Causal inference, or the problem of causality in general, has received a lot of attention in...

Causal inference in statistics: An overview

Abstract: 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 analysis to causal analysis of multivariate data. Special...

Causal inference in AI

Causal inference in AI refers to the process of drawing a conclusion based on the causal connection amongst the conditions of the occurrence of an effect. The main goal of causal inference is to analyze the response of the effect variable when the cause...

Causal inference without graphs

In a recent posting on this blog, Elias and Bryant described how graphical methods can help decide if a pseudo-randomized variable, Z, qualifies as an instrumental variable, namely, if it satisfies the exogeneity and exclusion requirements associated...

A Crash Course in Good and Bad Control

Carlos Cinelli, Andrew Forney and Judea Pearl Introduction If you were trained in traditional regression pedagogy, chances are that you have heard about the problem of “bad controls”. The problem arises when we need to decide whether the addition...

Lord’s Paradox: The Power of Causal Thinking

Background This post aims to provide further insight to readers of “Book of Why” (BOW) (Pearl and Mackenzie, 2018) on Lord’s paradox and the simple way this decades-old paradox was resolved when cast in causal language. To recap, Lord’s paradox...

Graphical Models and Instrumental Variables

At the request of readers, we re-post below a previous comment from Bryant and Elias (2014) concerning the use of graphical models for determining whether a variable is a valid IV. Dear Conrad,Following your exchange with Judea, we would like to present...

CAUSAL INFERENCE SUMMER SHORT COURSE AT HARVARD

We are informed of the following short course  at Harvard. Readers of this blog will probably wonder what this Harvard-specific jargon is all about, and whether it has a straightforward translation into Structural Causal Models. It has! And one of...