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Existing matrix completion methods are too blunt to estimate individual treatment effects, but this new estimator provides sharp, row-wise guarantees that unlock heterogeneous treatment effect estimation.
Solved: the precise conditions for efficiently estimating a Gaussian mean from coarse, partitioned data, closing a key gap in our understanding of learning from limited information.
