Question regarding imbalance in predictor variable categories

[micakt]

Good afternoon,

It is my understanding that large imbalances in predictor variable categories (e.g., a sample with 10% males and 90% females) will reduce a variable's importance (variable importance measure) and influence whether the causal forest can identify the contribution of treatment effect heterogeneity from that variable (if any). Are there any straightforward approaches to solving the problem of variable imbalance?

Thank you for your insight!

[erikcs]

Hi @micakt, imbalanced predictor variable categories isn't necessarily a problem for causal forest. In facts, it's quite plausible that HTEs are concentrated among "narrowly" defined predictor variables and nonparametric approaches like grf are more likely to find them as they explore many possible interactions.