Winsorizing and trimming in RCTs
研究了随机对照试验中缩尾和截尾处理异常值的两种方法，发现分层处理比整体处理更能减少估计偏差和II类错误，但会增加I类错误风险。
Winsorizing and trimming are used to minimize the effects of outliers on estimated treatment effects. In Randomized Controlled Trials (RCTs), the typical approach winsorizes/trims the tails of the whole sample, pooling together treatment and control groups. This can have as a consequence that observations from treatment and control groups are disproportionately winsorized/trimmed. An alternative approach, Stratified Winsorizing/Trimming, winsorizes treatment groups separately, ensuring that an equal proportion of observations are winsorized/trimmed per experimental arm. A formal framework and Monte Carlo simulations of an RCT illustrate that Stratified Winsorizing/Trimming reduces the treatment effect bias and risk of Type II errors compared to the traditional approach, although at the cost of a greater likelihood of Type I errors. Applications to Angelucci et al. (2023) and Jack et al. (2023) illustrate that the chosen winsorizing/trimming technique can affect the magnitude and statistical significance of treatment effects. Practical guidelines for researchers conducting RCTs that want to winsorize/trim outliers are discussed.