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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 Machine Learning
🐣 Hot Topic Early Bird — causal inference
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Speech & Audio

Authors

Karthika Mohan , Judea Pearl

Topics

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

Keywords

causal inference probabilistic modeling conditional probability graphical model causal effect missing datum conditional probability distribution
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