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Causal Inference in Machine Learning

18 Apr, 2020

Ricardo Silva Department of Statistical Science and Centre for Computational Statistics and Machine Learning [email protected]   Researchers reviewed 47 nutrition studies and concluded that children and adolescents who ate breakfast had better...

Causal inference in AI

17 Apr, 2020

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

17 Apr, 2020

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

14 Aug, 2019

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

13 Aug, 2019

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

01 Jun, 2019

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...


19 Mar, 2019

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...

On Imbens’s Comparison of Two Approaches to Empirical Economics

29 Jan, 2020

Many readers have asked for my reaction to Guido Imbens’s recent paper, titled, “Potential Outcome and Directed Acyclic Graph Approaches to Causality: Relevance for Empirical Practice in Economics,” arXiv.19071v1 [stat.ME] 16 Jul 2019. The note...