This paper investigates double/debiased machine learning (DML) under multiway clustered sampling environments. We propose a novel multiway cross fitting algorithm and a multiway DML estimator based on this algorithm.  Simulations indicate that the proposed procedure has favorable finite sample performance  .
This paper investigates double/debiased machine learning (DML) under multiway clustered sampling environments. We propose a novel multiway cross fitting algorithm and a multiway DML estimator based on this algorithm. We also develop a multiway cluster robust standard error formula. Simulations indicate that the proposed procedure has favorable finite sample performance . Applying the proposed method to market share data for demand analysis, we obtain larger two-way cluster robust standard errors than non-robust ones .
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We also develop a multiway cluster robust standard error formula.
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. Applying the proposed method to market share data for demand analysis, we obtain larger two-way cluster robust standard errors than non-robust ones