Causal Inference
Causal inference aims to determine cause-and-effect relationships from data, going beyond mere correlations to understand how interventions impact outcomes. Current research heavily focuses on addressing challenges like confounding (the influence of unobserved variables), particularly in high-dimensional data and complex treatments (e.g., text, sequences of actions), employing methods such as structural causal models, Bayesian Additive Regression Trees (BART), and various neural network architectures including Graph Neural Networks (GNNs). These advancements are crucial for improving the reliability of causal conclusions across diverse fields, from medicine and economics to personalized interventions and policy-making.
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Papers - Page 9
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C-XGBoost: A tree boosting model for causal effect estimation
Niki Kiriakidou, Ioannis E. Livieris, Christos DiouUnveiling the Impact of Macroeconomic Policies: A Double Machine Learning Approach to Analyzing Interest Rate Effects on Financial Markets
Anoop Kumar, Suresh Dodda, Navin Kamuni, Rajeev Kumar Arora
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Applied Causal Inference Powered by ML and AI
Victor Chernozhukov, Christian Hansen, Nathan Kallus, Martin Spindler, Vasilis SyrgkanisA Data-Driven Two-Phase Multi-Split Causal Ensemble Model for Time Series
Zhipeng Ma, Marco Kemmerling, Daniel Buschmann, Chrismarie Enslin, Daniel Lütticke, Robert H. Schmitt