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         Department of Systems Engineering and Engineering Management

                             The Chinese University of Hong Kong

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Date: 14:00 pm - 15:15 pm on 15 December (Monday)

Venue: ERB 513, The Chinese University of Hong Kong

Title: Imputation-Powered Inference for Missing Covariates

Speaker: Junting Duan, Department of Management Science and Engineering,

Stanford University





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.



Bio:

Junting Duan is a PhD candidate in the Department of Management Science

and Engineering at Stanford University, advised by Prof. Markus Pelger.

Her research interests lie broadly in data-driven decision-making,

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 and revisions in leading journals

including Management Science and the Journal of Econometrics, as well as

presentations at major conferences. Prior to Stanford, Junting received

her B.S. in Mathematics and Applied Mathematics from Peking University

with highest honors.



Everyone is welcome to attend the talk!