  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.
