RCT helps you focus on the statistics of the randomized control trials, rather than the heavy programming lifting. Any social scientist should use RCT when conducting Field Experiments.

RCT helps you in the whole process of designing and evaluating a RCT.

Let the steps of the design be a RCT:

This library has a function called
**summary_statistics** to know the distribution of all
covariates in data.

The function **tau_min** calculates the minimum
detectable treatment effect given power, significance level, outcome
variable and number of observations. This function computes this for any
share of control observations.

The function **N_min** calculates the minimum population
needed to identify a target tau_min

Prior to random assignment, one has to decide which
**categorical** variables to build blocks. Hence, the
blocks or strata are the group that combine every categorical variable.
The cardinality of this groups are all the possible combinations of the
chose categorical variables.

To build categorical variables in a powerful way, function
**n_tile_label** divides variables in the decided n groups,
putting label of the range of each category to the variable.

Once we have the blocking variables, we need to assign treatment
status **within** each strata. Function treatment_assign
performs such random assignment for any given number of treatment
groups. Furthermore, it handles misfits.

Misfits are defined as observations within each strata that are not really randomly assigned because when dividing the size of each strata N_strata to each treatment share, there are some remainder observations.

For instance, let the following example:

N_strata = 10 share_control = \(\frac{1}{3}\) share_treat_1 = \(\frac{1}{3}\) share_treat_2 = \(\frac{1}{3}\)

First 3 units are assigned to control, second 3 units are assigned to treat 1 and the last 3 unit are assigned to treat 2. As you already notice, the last observation is the remainder. This is a misfit. Misfits alter the successful random assignment because they are not. In the example, this observation is assigned to treat 2 non-randomly.

The function **treatment_assign** handles misfits in
three ways.

“NA” assigns the misfits to NAs, leaving the experiment with only the pure assigned observations.

“global” puts together the misfits of each strata into a single group and then assigns them randomly

“strata” assigns misfits to treatment within each strata

After running a RCT, the social scientist wants to know the ATE for one or several variables and the distribution of this impact within the blocking variables to check for Heterogenous Treatment Effects. Additionally, if the experiment lasted for more than one period and panel-data is available, one must cluster the standard errors by each i unit and control for period fixed effects. Finally, if by chance one o more covariates are not balance, one would like to control for them.

Function **impact_eval** does all this jobs in one
single command. It runs all the ATE regressions for each endogenous
variable, all the combinations of endogenous variables*heterogenous
variables.

For each combination the model run is:

\[Y_i = \alpha + \tau treatment + \epsilon \]