Congratulations to graduate student Naijia Liu, a co-recipient of the Best Poster Award at the joint conference of the sixth annual Asian Political Methodology Meeting and the second annual meeting of the Japanese Society for Quantitative Political Science.

According to the selection committee, "Liu presented her poster titled "Honest Inference on Missing Data." Existing methods for missing data have their shortcomings: information loss, over-fitting due to cyclic usage of data and excessive distributional assumption. To address these concerns, she proposes an honest inference method which assumes a probability distribution of the data and a missing at random scheme as well as conducts double machine learning procedure on the observed and missing groups, to obtain the bias-correction term for mean imputation. Multiple sample-splitting is conducted throughout the method for the consideration of variance estimation. Her simulation result illustrates superiority of her method."