Simple Imputation Rules for Prediction with Missing Data: Theoretical Guarantees vs. Empirical Performance

Dimitris Bertsimas, Arthur Delarue, Jean Pauphilet

Abstract: Missing data is a common issue in real-world datasets. This paper studies the performance of impute-then-regress pipelines by contrasting theoretical and empirical evidence. We establish the asymptotic consistency of such pipelines for a broad family of imputation methods. While common sense suggests that a 'good' imputation method produces datasets that are plausible, we show, on the contrary, that, as far as prediction is concerned, crude can be good. Among others, we find that mode-impute is asymptotically sub-optimal, while mean-impute is asymptotically optimal. We then exhaustively assess the validity of these theoretical conclusions on a large corpus of synthetic, semi-real, and real datasets. While the empirical evidence we collect mostly supports our theoretical findings, it also highlights gaps between theory and practice and opportunities for future research, regarding the relevance of the MAR assumption, the complex interdependency between the imputation and regression tasks, and the need for realistic synthetic data generation models.

Submission Length: Regular submission (no more than 12 pages of main content)

Changes Since Last Submission:

(23 April) Preliminary revision addressing most reviewers' initial comments, with edits indicated in blue.

(29 April) Updated results with NN as a predictor. Additional reporting on Table 1/2 for real datasets. Edits since original submission indicated in blue.

(2 May) Updated literature review and discussion sections. Edits since original submission in blue.

(30 May) Accepted.

Code: https://github.com/adelarue/PMD

Assigned Action Editor: ~Shinichi_Nakajima2

Submission Number: 2412