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2018 PGM PGM 2018

Consistent Estimation given Missing Data

Abstract

This paper presents a unified approach for recovering causal and probabilistic queries using graphical models given missing (or incomplete) data. To this end, we develop a general algorithm that can recover conditional probability distributions and conditional causal effects in semi-Markovian models.

🌉 Interdisciplinary Bridge - Artificial Intelligence and Knowledge & Reasoning

🐣 Hot Topic Early Bird - probabilistic modeling

Authors

Karthika Mohan, Judea Pearl

Topics

Artificial Intelligence > Core AI > Causal Inference Artificial Intelligence > Bayesian & Probabilistic > Probabilistic Modeling Knowledge & Reasoning > Reasoning > Causal Inference Machine Learning > Bayesian & Probabilistic > Probabilistic Modeling Machine Learning > Core Methods > Graphical Models

Keywords

causal inference probabilistic modeling conditional probability graphical model causal effect missing data conditional probability distribution semi-markovian model

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