Imputing missing values of one of the independent variable using dependent variable in addition to other independent variables?

Question

I want to impute missing values of an independent variable say variable X1, the other independent variables are weakly related to X1. However, the dependent variable has strong relation with X1.

I wish to use sklearn IterativeImputer's missing value imputation estimators like KNN regressor or ExtraTreesRegressor (similar to missforest in R).

https://scikit-learn.org/stable/auto_examples/impute/plot_iterative_imputer_variants_comparison.html#sphx-glr-auto-examples-impute-plot-iterative-imputer-variants-comparison-py

Can I use dependent variable in addition to independent variables to impute values of X1? Will this introduce too much variance in my model? If this isn't recommended then how should X1 be treated, deletion of X1 is not an option and I fear if I impute X1 missings with only other IV's the imputed values would not be moderately accurate.

Thanks

Answer 1

This is recommended and has been demonstrated on theoretical (Meng, 1994) and empirical (Kontopantelis et al, 2017) grounds to be effective. You should use as many strong predictors of the missing values as possible in your imputation regardless of whether you're using them in the analysis. For example, if you were studying the relationship between baseline measured and 6-month outcomes but also had 3-month and 12-month outcomes that you were otherwise ignoring, you should use those outcomes to impute any missing values (if the variables are related).

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Kontopantelis, E., White, I. R., Sperrin, M., & Buchan, I. (2017). Outcome-sensitive multiple imputation: A simulation study. BMC Medical Research Methodology, 17(1). https://doi.org/10.1186/s12874-016-0281-5

Meng, X.-L. (1994). Multiple-Imputation Inferences with Uncongenial Sources of Input. Statistical Science, 9(4), 538–558.