Identification of and Correction for Publication Bias
提出两种识别发表概率的方法，分别基于系统复制研究和元研究，并给出偏倚校正估计量和置信区间，应用于实验经济学、心理学和最低工资效应的元研究。
Some empirical results are more likely to be published than others. Selective publication leads to biased estimates and distorted inference. We propose two approaches for identifying the conditional probability of publication as a function of a study’s results, the first based on systematic replication studies and the second on meta-studies. For known conditional publication probabilities, we propose bias-corrected estimators and confidence sets. We apply our methods to recent replication studies in experimental economics and psychology, and to a meta-study on the effect of the minimum wage. When replication and meta-study data are available, we find similar results from both.