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Graphical models, causal inference, and econometric models

Pages 3-34 | Published online: 17 Feb 2007

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J. Wesley Burnett & Calvin Blackwell. (2023) Graphical causal modelling: an application to identify and estimate cause-and-effect relationships. Applied Economics 0:0, pages 1-15.
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Duo Qin. (2019) Let’s take the bias out of econometrics. Journal of Economic Methodology 26:2, pages 81-98.
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Momchil S. Tomov, Pedro A. Tsividis, Thomas Pouncy, Joshua B. Tenenbaum & Samuel J. Gershman. (2023) The neural architecture of theory-based reinforcement learning. Neuron 111:8, pages 1331-1344.e8.
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V. Vilasini & Roger Colbeck. (2022) Impossibility of Superluminal Signaling in Minkowski Spacetime Does Not Rule Out Causal Loops. Physical Review Letters 129:11.
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V. Vilasini & Roger Colbeck. (2022) General framework for cyclic and fine-tuned causal models and their compatibility with space-time. Physical Review A 106:3.
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Wenkai Xu, Gang Niu, Aapo Hyvärinen & Masashi Sugiyama. (2021) Direction Matters: On Influence-Preserving Graph Summarization and Max-Cut Principle for Directed Graphs. Neural Computation 33:8, pages 2128-2162.
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Pedro A. Valdes-Sosa. (2015) The many levels of causal brain network discovery. Physics of Life Reviews 15, pages 145-147.
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Özgür Kabak, Füsun Ülengin, Şule Önsel, Özay Özaydin & Emel Aktaş. (2014) Cumulative belief degrees approach for analyzing the competitiveness of the automotive industry. Knowledge-Based Systems 70, pages 15-25.
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Tomáš Paus. (2010) Population neuroscience: Why and how. Human Brain Mapping 31:6, pages 891-903.
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J.W. Kim, D.J. Leatham & D.A. Bessler. (2007) REITs’ dynamics under structural change with unknown break points. Journal of Housing Economics 16:1, pages 37-58.
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