Causal mediation

[@NathalieTamayo]

Thank you for such a clear explanation.

[@mronkko]

You are welcome!

[@jameshurley1302]

brilliant - 27 min master class!

[@mronkko]

Thanks. I appreciate the term "master class" ;)

[@Rezayyyyyyyyy]

Hi, thanks for the video. Could you please active the auto-subtitle for this video?

[@mronkko]

The automatic captions are added automatically but for some reason KZbin does not auto caption all videos. The only way yo get automatic captions to this video is to reupload it, as far as I know. Human-generated captions will be added in a day or two.

[@mronkko]

The video is now captioned, but the captions have not been checked yet.

[@moonman9318]

Damn good. Appreciate this.

[@mronkko]

You are welcome

[@goncamert7375]

Thank you very much for the video. It is quite helpful. However, I want to ask you if mediation analysis works, when we have multiple (2 sequential count) mediators and two X variables (both are factor and one has 19, the other has 33 classes). How can we analyse this kind of problem? I would be appreciated any comments in advance.

[@mronkko]

Yes, you can use mediation analysis in that case. If your X variables are unordered categories, I would recommend modeling X-M relationships with linear regression. Do you mean your model is X-->M1->M2->Y, or do the M1 and M2 work in parallel representing two different causal mechanisms? If you can justify linear functional forms between the variables, specifying a mediation model is simple. (note that count variables can be modeled with linear regression contrary to common belief, see journals.sagepub.com/doi/full/10.1177/1094428121991907).

[@goncamert7375]

@@mronkko Thank you very much for your reply. I will try modelling linear relationship between X and M. My model is X-->M1->M2->Y. Thanks for the references indeed!

[@mronkko]

@@goncamert7375 If you can model linear between M1-M2, then things are simple. If not, the approach that I explain should still work easily because of linear X->M1

[@videoveiwer]

THANK YOU SIR

[@mronkko]

Most welcome