CIBMTR - Equity in post-HCT Survival Predictions #13 How to make sense of the race group distribution in the data?

https://dongsunseng.com/entry/CIBMTR-Equity-in-post-HCT-Survival-Predictions-11-ESP-EDA-which-makes-sense-%E2%AD%90%EF%B8%8F%E2%AD%90%EF%B8%8F%E2%AD%90%EF%B8%8F%E2%AD%90%EF%B8%8F%E2%AD%90%EF%B8%8F-AFT-Loss-func-sol-1

CIBMTR - Equity in post-HCT Survival Predictions #11 ESP EDA which makes sense ⭐️⭐️⭐️⭐️⭐️ (AFT Loss func sol

From my other blog post, we discussed about

This blog is about the "further discussion": https://www.kaggle.com/competitions/equity-post-HCT-survival-predictions/discussion/550302

CIBMTR - Equity in post-HCT Survival Predictions

How to make sense of the race group distribution in the data ?

Counting values of race groups I get the following:

• Having worked on the topic of equity for sensitive applications, I have found one of the main problem to be imbalance in data of interest.
• Typically some less represented races will end up with wider estimates.
• However the data at hand seems to have been resampled (or generated as balanced).
• While this can be achieved on real data by downsampling the majority class, it usually kills representativeness of the population.
• I am concerned a model optimised with this metric on this balanced dataset would perform worse on real life 'race imbalanced' data.
• How does 'race-balancing' the dataset make sense in an equity competition ?

Comments:

• Maybe the idea behind balanced, synthetic data is to accentuate differences in risk prediction due only to the available features, by taking imbalance out of the problem.
  • By eliminating racial imbalances in the actual data, one can more clearly see differences in risk predictions that are "purely attributable to available features"
  • This allows for more accurate evaluation of actual prediction performance differences rather than differences in population ratios
• This could suggest a need for additional predictors if certain groups are more poorly predicted.
  • If predictions are less accurate for certain groups, this could indicate that current features don't adequately explain those groups
  • This could signal the need for additional predictors that better characterize these groups

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