In this final post we outline the typical flow of a causal study in relations to the main tools provided by *Causalinference*. At a high level, assuming that unconfoundedness holds true in the given problem, we can break down a typical study into two phases.

In the **design** phase, we inspect and manipulate the data set to ensure that the most credible analysis can be conducted on it. We achieve this by proceeding in the following steps:

- Assess covariate balance with
`summary_stats`

. If the normalized differences in covariate means suggest severe covariate imbalance, we can try to address it by using the propensity-score-based techniques below. - Estimate propensity score with
`est_propensity_s`

, so that the following two propensity methods can be employed. - Trim sample with
`trim_s`

to exclude subjects with extreme propensity scores. Since very little can be credibly said about such units, we should focus attention on the remaining units that exhibit a higher degree of covariate balance. - Stratify sample with
`stratify_s`

to group similar subjects together and improve within-bin covariate balance.

In the **analysis** phase, we estimate treatment effects using a number of reasonable estimators. The estimators with the most desirable properties are

- The blocking estimator invoked by
`est_via_blocking`

, which aggregates the least squares estimates within each propensity bin to produce an overall average treatment effect estimate. - The matching estimator invoked by
`est_via_matching`

, which pairs subjects together via nearest-neighborhood matching to arrive at an overall average treatment effect estimate. Since bias can result when the matching is imperfect, bias correction is recommended.

If the design phase is done well, the two estimates recommended above should result in similar estimates due to their stability properties.

We conclude by noting that additional checks and tests exist for assessing the validity of a causal study. Some of these tests (e.g. for assessing the unconfoundedness assumption) actually require no additional tools beyond what is provided in *Causalinference*. For the interested reader, please refer to Imbens (2014).

### References

Imbens, G. (2014). Matching methods in practice: Three examples. *NBER Working Paper No. 19959*.