algorithmic-governance-audit

Causal Forest with Double Machine Learning pipeline for estimating heterogeneous treatment effects of Chinese agricultural policy reforms on county-level structural transformation, using county-year panel data (2000-2023). Covers three quasi-natural experiments (2006 tax abolition, 2016 supply-side structural reform, 2014 targeted poverty alleviation) with a full robustness, heterogeneity, targeting, and mechanism suite.

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Keywords

heterogeneous-treatment-effects, agricultural-policy, algorithmic-targeting, causal-forest, double-machine-learning, structural-transformation

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