I have the dataset for the health of patients along with various treatments they were given.
In a normal case, I would just use linear regression to fit a model [y ~ t1 + t2 + t3 ... +tn]. This will give me the correlation of each treatment t with the health of patient y.
What I want to do is to identify the causal effect instead of just correlation of each treatment.
Upon looking on the internet and doing some readings, I have understood that I need to match the data before feeding it to a linear model (propensity score matching etc.)
There are methods out there which are used to match data , but only work when there is 1 treatment variable. i.e y~ t1. There are other methods aswell for multiple treatments but assume that only 1 treatment is being applied at one time.
In my case several of the treatments are being applied at the same time and hence the above mentioned methods do not work for my study.
Can anyone explain to me which method I should go about using to match the data if I have multiple treatments and several of them being applied at the same time.
Thanks in advance.