Nearest neighbor (NN) matching is widely used in observational studies for causal effects. Abadie and Imbens (2006) provided the first large‐sample analysis of NN matching. Their theory focuses on the case with the number of NNs, M fixed. We reveal something new out of their study and show that once allowing M to diverge with the sample size an intrinsic statistic in their analysis constitutes a consistent estimator of the density ratio with regard to covariates across the treated and control groups. Consequently, with a diverging M , the NN matching with Abadie and Imbens' (2011) bias correction yields a doubly robust estimator of the average treatment effect and is semiparametrically efficient if the density functions are sufficiently smooth and the outcome model is consistently estimated. It can thus be viewed as a precursor of the double machine learning estimators.

Abstract We document distinctly different clientele effects on investor attention and return responses to information. Macro news crowds out retail investor attention to firms’ earnings news by 49%. For stocks with high retail ownership, macro news dampens earnings announcement returns by 17% and substantially increases post-announcement drift, especially during high VIX periods. In contrast, macro news increases institutional investor attention to scheduled earnings announcements but not their attention to unscheduled analysts’ forecast revisions. The findings confirm the implications of rational inattention models and highlight the importance of considering clientele effects in understanding the effect of news on attention and asset prices.