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Structural Equation/Causal Models (SEMs/SCMs) are widely used in epidemiology and social sciences to identify and analyze the average causal effect (ACE) and conditional ACE (CACE).
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A New Approach to Causal Inference in Mortality Studies with a Sustained Exposure Period—Application to Control of the Healthy Worker Survivor Effect
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The Consistency Statement in Causal Inference: A Definition or An Assumption?
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Causal Inference: What If
Hernán, M. A.; and Robins, J. M. 2009 · 2009
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Density Estimation by Dual Ascent of the Log-Likelihood
Tabak, E.; and Vanden-Eijnden, E. 2010 · 2010
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Neighborhood Effects in Temporal Perspective: The Impact of Long-Term Exposure to Concentrated Disadvantage on High School Graduation
Wodtke, G. T.; Harding, D. J.; and Elwert, F. 2011 · 2011
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The D o Do -Calculus Revisited
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Uria, B.; Murray, I.; and Larochelle, H. 2013 · 2013
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Unconstrained Monotonic Neural Networks
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Kingma, D. P.; Salimans, T.; Jozefowicz, R.; Chen, X.; Sutskever, I.; and Welling, M. 2016 · 2016
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Masked Autoregressive Flow for Density Estimation
Papamakarios, G.; Murray, I.; and Pavlakou, T. 2017 · 2017
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Paszke, A.; Gross, S.; Chintala, S.; Chanan, G.; Yang, E.; DeVito, Z.; Lin, Z.; Desmaison, A.; Antiga, L.; and Lerer, A. 2017 · 2017
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The Book of Why: The New Science of Cause and Effect
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