I have temporal twitter data, and I want to calculate propensity score for the treatment and control group. The problem is, the treatment happened at different time for different user, and I want to compare aggregated values for covariates. For example, for user
a1, treatment occurred after six months of account creation, for user
a2, treatment occurred after eight months. I want to take the average number of tweets posted by those users before the treatment (for first six and eight months respectively), and find similar users in the control group who posted similar number of tweets for six and eight months. I have other covariates (e.g. average number of hashtags used). Is propensity score matching a suitable approach for this? If so how can I do that?
One approach came to my mind is to find match for each sample in the treatment group individually. For example, first for
a1 find match someone in the control group who has similar average tweet for first six months. Then for
a2 someone in the control group who has similar average tweet for first eight months and so on. But not sure if this is a valid approach. If not what else I can do?
In our experiment, the treatment is the first tweet of an user that was retweeted more than a certain number of times. We want to compare for example, average number of tweets posted before and after the treatment by a user in treatment group with someone in the control group.