Abstract: Missing covariate data is a prevalent problem in empirical research. We provide a novel framework for handling missing covariate data for estimation and inference in downstream tasks. Our general framework provides an automatic and easy-to-use pipeline for empirical researchers: First, missing values are imputed using virtually any imputation method under general observation patterns. Second, we automatically correct for the imputation bias and adaptively weight the imputed values according to their quality. Third, we use all available data, including imputed observations, to obtain more precise point estimates for the downstream task with valid confidence intervals. Our approach ensures valid inference while improving statistical efficiency by leveraging all available data. We establish the asymptotic normality of the proposed estimator under general missing data patterns and a broad class of imputation methods. Through simulations, we demonstrate the superior performance of our approach over natural benchmarks, as it achieves both lower bias and variance while being robust to imputation quality. In a comprehensive empirical study of the dependence of equity markets on carbon emissions, we show that properly accounting for missing emissions data yields no evidence of correlation between stock returns and emissions directly produced by companies, but a negative correlation with value chain emissions.

Bios: Junting Duan will join the University of Hong Kong Business School as an Assistant Professor in the Innovation and Information Management Department in July 2026. She is currently a PhD candidate in the Department of Management Science and Engineering at Stanford University, advised by Professor Markus Pelger. Her research lies broadly in data-driven decision-making and focuses on developing statistical and machine learning methods with rigorous theoretical foundations for applications in finance and causal inference. In particular, she specializes in factor modeling, missing data solutions, and causal machine learning. Junting’s work has been recognized through publications in leading journals including Management Science and the Journal of Econometrics, as well as invitations to present at major conferences such as the American Economic Association Annual Meeting, the NBER-NSF Time-Series Conference, the NBER Forecasting & Empirical Methods Conference, and the INFORMS Annual Meeting.