Using machine learning for efficient flexible regression adjustment in economic experiments
研究了如何利用机器学习方法，通过估计条件期望函数来最优地使用协变量，从而降低实验数据分析中的方差，并提出了一个自动实现、渐近有效的回归调整估计量。
This study investigates the optimal use of covariates in reducing variance when analyzing experimental data. We show that finding the variance-minimizing strategy for making use of pre-treatment observables is equivalent to estimating the conditional expectation function of the outcome given all available pre-randomization observables. This is a pure prediction problem, which recent advances in machine learning (ML) are well-suited to tackling. Through a number of empirical examples, we show how ML-based regression adjustments can feasibly be implemented in practical settings. We compare our proposed estimator to other standard variance reduction techniques in the literature. Two important advantages of our ML-based regression adjustment estimator are that (i) they improve asymptotic efficiency relative to other alternatives and (ii) they can be implemented automatically, with relatively little tuning from the researcher, which limits the scope for data-snooping.