Vectorize matching and ATE computation to speed up computation

[jcreinhold]

Is your feature request related to a problem? Please describe.
dowhy is a great package, but the matching methods are unnecessarily slow for large datasets.

Describe the solution you'd like
In propensity score matching and distance matching, the matching and ATT/ATC computation is done in a Python for loop which gets prohibitively time-consuming for large datasets (e.g., here). It's also unnecessary.

For example, we can change this:

for i in range(numtreatedunits):
    treated_outcome = treated.iloc[i][self._target_estimand.outcome_variable[0]].item()
    control_outcome = control.iloc[indices[i]][self._target_estimand.outcome_variable[0]].item()
    att += treated_outcome - control_outcome

To something like this (this is untested, but something like this should work):

outcome_variable = self._target_estimand.outcome_variable[0]
treated_outcome = treated[outcome_variable]
control_outcome = control.iloc[indices][outcome_variable]
att = (treated_outcome - control_outcome).mean().item()

For datasets with >100k observations, this can provide a large speed-up.

Describe alternatives you've considered

Leaving it as is. There isn't much reason to leave this as is. We already are using numpy and pandas so we should make use of the constructs available in them to avoid the Python interpreter overhead.

Additional context
I can submit a PR if this is acceptable.