Causality Modeling and Statistical Generative Mechanisms

DOI10.1007/978-3-319-99492-5_7zbMath1518.68313OpenAlexW2888346852MaRDI QIDQ6104516

Igor Mandel

Publication date: 28 June 2023

Published in: Braverman Readings in Machine Learning. Key Ideas from Inception to Current State (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-319-99492-5_7

zbMATH Keywords

statistical inferencestatistical learningdependency modelingcounterfactual statementscausality modelinggenerative statistical mechanismsintrinsic probability

Mathematics Subject Classification ID

Foundations and philosophical topics in statistics (62A01) Learning and adaptive systems in artificial intelligence (68T05) Nonparametric inference (62G99) Causal inference from observational studies (62D20)

Cites Work

• Radiation risk estimation. Based on measurement error models
• Core concepts in data analysis. Summarization, correlation and visualization.
• Data aggregation and Simpson's paradox gauged by index numbers
• Kernel methods in machine learning
• Confounding and collapsibility in causal inference
• Solving the omitted variables problem of regression analysis using the relative vertical position of observations
• Computer Age Statistical Inference
• Matched Sampling for Causal Effects
• Measurement Error
• Analysis of regression in game theory approach
• Causal Inference for Statistics, Social, and Biomedical Sciences
• Semi-supervised Learning in Causal and Anticausal Settings
• Estimation of Dependences Based on Empirical Data
• Measurement Error in Nonlinear Models
• Digraphs
• Some Estimators for a Linear Model with Random Coefficients
• The ASA Statement on p-Values: Context, Process, and Purpose
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