You are welcome. The hoodie is an old model of our university hoodie and I got three of them from discount to always have something to wear in the studio.
Thank you so much for this content. I am finishing my doctoral thesis work and this has been so helpful for me to better understand the differences in the estimation of the mediation models under a non-linear framework. I was wondering if you had any thoughts on mediation analysis when both the mediator and the outcome are modeled as time-to-event outcomes. If we face similar issues as in the non-linear setting you discussed or entirely new issues when trying to interpret the relative NDE and NIE. Again thanks for the content.
Sorry for a delayed response. In your case, you need to clearly define what the causal effect of interest is conceptually. For example, if you are assuming that X causes M with some delay and M cause Y with some delay, you could be intersted in the total time for Y to occur and how it depends on the value of X. I would start by predicting the time to Y with different levels of X and M and then comparing. But this really depends on your research question.
Thank you for the excellent explanation of causal mediation in your two videos! Since I don't have a background in data science, but rather applied science, I still have a very basic question. Can causal mediation be used with non-manipulated variables. when x is not a treatment, but just an observed variable?
![ПОЛНАЯ ИСТОРИЯ ЭКЗОРЦИЗМА [Топ Сикрет]](http://i.ytimg.com/vi/qBySr6Bt0B8/mqdefault.jpg)
your blue hoodie is iconic at this point. thanks as always!
You are welcome. The hoodie is an old model of our university hoodie and I got three of them from discount to always have something to wear in the studio.
This has made my life so much easier!!! thank you very much
You are welcome
Thank you so much for this content. I am finishing my doctoral thesis work and this has been so helpful for me to better understand the differences in the estimation of the mediation models under a non-linear framework. I was wondering if you had any thoughts on mediation analysis when both the mediator and the outcome are modeled as time-to-event outcomes. If we face similar issues as in the non-linear setting you discussed or entirely new issues when trying to interpret the relative NDE and NIE. Again thanks for the content.
Sorry for a delayed response. In your case, you need to clearly define what the causal effect of interest is conceptually. For example, if you are assuming that X causes M with some delay and M cause Y with some delay, you could be intersted in the total time for Y to occur and how it depends on the value of X. I would start by predicting the time to Y with different levels of X and M and then comparing. But this really depends on your research question.
Thank you for the excellent explanation of causal mediation in your two videos! Since I don't have a background in data science, but rather applied science, I still have a very basic question. Can causal mediation be used with non-manipulated variables. when x is not a treatment, but just an observed variable?
Yes you can. However, all caveats to making causal claims from observational data apply.