Multimodal learning faces challenges when modalities differ between development and deployment due to various factors, including perceived informativeness and missing data. The framework ICYM2I (In Case You Multimodal Missed It) is introduced to address biases in estimating information gain from modalities under missingness, using inverse probability weighting-based correction. The effectiveness of this approach is demonstrated through synthetic and real-world medical datasets.