I consider the estimation of the average treatment effect (ATE), in a population that can be divided into G groups, and such that one has unbiased and uncorrelated estimators of the conditional average treatment effect (CATE) in each group. These conditions are for instance met in stratified randomized experiments. I first assume that the outcome is homoscedastic, and that each CATE is bounded in absolute value by B standard deviations of the outcome, for some known constant B. I derive, across all linear combinations of the CATEs' estimators, the estimator of the ATE with the lowest worst-case mean-squared error. This optimal estimator assigns a weight equal to group g's share in the population to the most precisely estimated CATEs, and a weight proportional to one over the CATE's variance to the least precisely estimated CATEs. This optimal estimator is feasible: the weights only depend on known quantities. I then allow for heteroskedasticity and for positive correlations between the estimators. This latter condition is often met in differences-in-differences designs, where the CATEs are estimators of the effect of having been treated for a certain number of time periods. In that case, the optimal estimator is no longer feasible , as it depends on unknown quantities, but a feasible estimator can easily be constructed by replacing those unknown quantities by estimators .
I consider the estimation of the average treatment effect (ATE), in a population that can be divided into G groups, and such that one has unbiased and uncorrelated estimators of the conditional average treatment effect (CATE) in each group. These conditions are for instance met in stratified randomized experiments. I assume that the outcome is homoscedastic, and that each CATE is bounded in absolute value by B standard deviations of the outcome, for some known constant B. I derive, across all linear combinations of the CATEs' estimators, the estimator of the ATE with the lowest worst-case mean-squared error. This optimal estimator assigns a weight equal to group g's share in the population to the most precisely estimated CATEs, and a weight proportional to one over the CATE's variance to the least precisely estimated CATEs. This optimal estimator is feasible: the weights only depend on known quantities. I then allow for positive covariances known up to the outcome's variance between the estimators. This condition is met in differences-in-differences designs, if errors are homoscedastic and uncorrelated. Under those assumptions, I show that the minimax estimator is still feasible and can easily be computed .
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first
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heteroskedasticity and for positive correlations
positive covariances known up to the outcome's variance
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latter condition is often
condition is
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where the CATEs are estimators of the effect of having been treated for a certain number of time periods. In that case, the optimal estimator is no longer feasible , as it depends on unknown quantities, but a feasible estimator
if errors are homoscedastic and uncorrelated. Under those assumptions, I show that the minimax estimator is still feasible and
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constructed by replacing those unknown quantities by estimators
computed