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potential-outcomes-framework

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emeyer-hansen / synthetic_causal_framework

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The Synthetic Causal Framework (SCF) is a novel methodological framework for deriving reliable and valid causal inference for non-manipulable phenomena through use of synthetic units.

natural-language-processing political-science causal-inference scf experimental-design sct quantitative-social-science large-language-model political-methodology synthetic-causal-framework synthetically-controlled-trial parallel-worlds-estimation synthetic-units simulated-units causal-framework causal-methodology causal-design potential-outcomes-framework fundamental-problem-of-causal-inference welfare-attitudes
  • Updated May 24, 2025
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emeyer-hansen / synthetic-causal-framework

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This repository is part of the Synthetic Causal Framework (SCF) project and is dedicated to publicly hosting the newest version of Synthetic Causal Framework

natural-language-processing political-science causal-inference scf experimental-design sct quantitative-social-science large-language-model political-methodology synthetic-causal-framework synthetically-controlled-trial parallel-worlds-estimation synthetic-units simulated-units causal-framework causal-methodology causal-design potential-outcomes-framework fundamental-problem-of-causal-inference welfare-attitudes
  • Updated May 24, 2025
  • HTML

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