A1701
Title: Matrix completion and causal inference
Authors: Jungjun Choi - University of Rhode Island (United States) [presenting]
Ming Yuan - Columbia University (United States)
Abstract: Matrix completion concerns the imputation of missing entries in a partially observed matrix. While its rapid development was originally motivated by applications in recommendation systems, it has also opened new possibilities in causal inference, where missing counterfactual outcomes can be imputed to estimate individual treatment effects. In causal inference problems, the missingness pattern in potential outcomes is induced by the treatment adoption process. Although treatment assignment may satisfy the missing-at-random (MAR) assumption in randomized experiments and certain quasi-experimental designs, this assumption is often violated in observational studies. For instance, when a program is introduced at a particular time for a subset of units, the potential outcomes under control become unobserved for treated units after treatment adoption, creating a block missingness structure. An inferential framework for matrix completion under missing-not-at-random (MNAR) mechanisms is presented and applied to treatment effect estimation. In particular, fixed effects are incorporated into the model, which is more appropriate for economic data, and the inclusion of these fixed effects is shown to improve estimation quality.
