In order for the estimand of the propensity score method to be unbiased, the following is sufficient:
(a) SUTVA (this is untestable)
(b) Conditional ignorability (this is testable in principle, but only if we randomize the exposure A)
(c) The treatment assignment probability model (that is the model for p(A | C), where A is exposure and C is baseline covariates) must be correct.
It may be that the “balance property” tests a part of (b), but surely not all of it! That is, the arms might look balanced, but conditional ignorability might still not hold. We cannot test all the assumptions we need to draw causal conclusions from observational data using only observational data. Causal assumptions have to enter in somewhere!
I think I might be confused about why checking for balance without working out the effect is an advantage—but I will think about it, because I am not an expert on propensity score methods, so there is probably something I am missing.
In order for the estimand of the propensity score method to be unbiased, the following is sufficient:
(a) SUTVA (this is untestable)
(b) Conditional ignorability (this is testable in principle, but only if we randomize the exposure A)
(c) The treatment assignment probability model (that is the model for p(A | C), where A is exposure and C is baseline covariates) must be correct.
It may be that the “balance property” tests a part of (b), but surely not all of it! That is, the arms might look balanced, but conditional ignorability might still not hold. We cannot test all the assumptions we need to draw causal conclusions from observational data using only observational data. Causal assumptions have to enter in somewhere!
I think I might be confused about why checking for balance without working out the effect is an advantage—but I will think about it, because I am not an expert on propensity score methods, so there is probably something I am missing.