I would suggest to match only on pre-treatment variables and not on post-treatment variables. If we match on post-treatment variables it is extremely plausible we induce selection bias in our sample and thus we get bogus insights. Selection bias can appear when one of the variables $C$ we use in matching is related to the treatment $A$ as well as the outcome of interest $Y$.

Notice that selection bias can be present not only because we have certain eligibility criteria (as those imposed by matching on a post-treatment variable $C$) but also through loss to follow-up; i.e. the drop-out rates (stopping the use of Twitter) might be different between the groups examined. The small paper on "Loss to follow-up" by Dettori offers a very brief, succinct introduction on this.

I would suggest to match only on pre-treatment variables and not on post-treatment variables. If we match on post-treatment variables it is extremely plausible we induce selection bias in our sample and thus we get bogus insights. Selection bias can appear when one of the variables $C$ we use in matching is related to the treatment $A$ as well as the outcome of interest $Y$.

More formally and quoting Hernan & Robins directly: "selection bias can be defined as the bias resulting from conditioning on the common effect of two variables, one of which is either the treatment or associated with the treatment, and the other is either the outcome or associated with the outcome". Chapt. 8 "Selection Bias" from their upcoming book "Causal Inference". Therefore conditioning on post-treatment variables is a clear case of conditioning on common effects.

Notice that selection bias can be present not only because we have certain eligibility criteria (as those imposed by matching on a post-treatment variable $C$) but also through loss to follow-up; i.e. the drop-out rates (stopping the use of Twitter) might be different between the groups examined. The small paper on "Loss to follow-up" by Dettori offers a very brief, succinct introduction on this.

I would suggest to match only on pre-treatment variables and not on post-treatment variables. If we match on post-treatment variables it is extremely plausible we induce selection bias in our sample and thus we get bogus insights. Selection bias can appear when one of the variables $C$ we use in matching is related to the treatment $A$ as well as the outcome of interest $Y$.

Notice that selection bias can be present not only because we have certain eligibility criteria (as those imposed by matching on a post-treatment variable $C$) but also through loss to follow-up; i.e. the drop-out rates (stopping the use of Twitter) might be different between the groups examined. The small paper on "Loss to follow-up" by Dettori offers a very brief, succinct, brief introduction on this.

I would suggest to match only on pre-treatment variables and not on post-treatment variables. If we match on post-treatment variables it is extremely plausible we induce selection bias in our sample and thus we get bogus insights. Selection bias can appear when one of the variables $C$ we use in matching is related to the treatment $A$ as well as the outcome of interest $Y$.

Notice that selection bias can be present not only because we have certain eligibility criteria (as those imposed by matching on a post-treatment variable $C$) but also through loss to follow-up; i.e. the drop-out rates (stopping the use of Twitter) might be different between the groups examined. The small paper on "Loss to follow-up" by Dettori offers a very brief, succinct introduction on this.