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Working Paper
Parallel Trends Forest: Data-Driven Control Sample Selection in Difference-in-Differences
This paper introduces parallel trends forest, a novel approach to selecting optimal control samples when using difference-in-differences (DiD) in a relatively long panel data with little randomization in treatment assignment. Our method uses machine learning techniques to find control units that best meet the parallel trends assumption. We demonstrate that our approach outperforms existing methods, particularly with noisy, granular data. Applying the parallel trends forest to analyze the impact of post-trade transparency in corporate bond markets, we find that it produces more robust ...