You can learn about it in this blog, but long story short, survival models (most often) predict a survival function. It tells us what is the probability of an event not happening until a given time t. The output can also be a single value (e.g., risk score) but these scores are always some aggregates of the survival function and this naturally leads to a loss of information included in the prediction.
The complexity of the output of survival models means that standard explanation methods cannot be applied directly.
Because of this, we (I and the team: Mateusz Krzyziński, Hubert Baniecki, and Przemyslaw Biecek) developed an R package — survex, which provides explanations for survival models. We hope this tool allows for more widespread usage of complex machine learning survival analysis models. Until now, simpler statistical models such as Cox Proportional Hazards were preferred due to their interpretability — vital in areas such as medicine, even though they were frequently outperformed by complex machine learning models.
Read on to dive into the topic. H/T R-Bloggers.