Graphical Models for Processing Missing Data

This paper reviews recent advances in missing data research using graphical models to represent multivariate dependencies. We first examine the limitations of traditional frameworks from three different perspectives: \textit{transparency, estimability...

Full Law Identification In Graphical Models Of Missing Data: Completeness Results

Razieh Nabi, Rohit Bhattacharya, Ilya Shpitser Missing data has the potential to affect analyses conducted in all fields of scientific study, including healthcare, economics, and the social sciences. Several approaches to unbiased inference in the presence...

CAUSAL RELATIONAL LEARNING

Babak Salimi, Harsh Parikh, Moe Kayali, Sudeepa Roy, Lise Getoor, Dan Suciu Causal inference is at the heart of empirical research in natural and social sciences and is critical for scientific discovery and informed decision making. The gold standard...

Causal Inference in Machine Learning

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...

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...