balance_regression

balance_regression() Runs a LPM of treatment status against all covariates (treatment~X'B).

Assists in the whole process of designing and evaluating Randomized Control Trials.
Robust treatment assignment by strata/blocks, that handles misfits;
Power calculations of the minimum detectable treatment effect or minimum populations;
Balance tables of T-test of covariates;
Balance Regression: (treatment ~ all x variables) with F-test of null model;
Impact_evaluation: Impact evaluation regressions. This function
gives you the option to include control_vars, fixed effect variables,
cluster variables (for robust SE), multiple endogenous variables and
multiple heterogeneous variables (to test treatment effect heterogeneity)
summary_statistics: Function that creates a summary statistics table with statistics
rank observations in n groups: Creates a factor variable with n groups. Each group has
a min and max label attach to each category.
Athey, Susan, and Guido W. Imbens (2017) <arXiv:1607.00698>.

Isidoro Garcia-Urquieta

Assign Treatments, Power Calculations, Balances, Impact
Evaluation of Experiments

balance_regression function

<dl><dt>data</dt>
<dd>A data.frame, tibble or data.table</dd>
<dt>treatment</dt>
<dd>a string with treatment status column</dd></dl>

Arguments

balance_regression() Runs a LPM of treatment status against all covariates (treatment~X'B). — balance_regression

<dl>

<dt>data</dt>
<dd>A data.frame, tibble or data.table</dd>


<dt>treatment</dt>
<dd>a string with treatment status column</dd>

</dl>

balance_regression: balance_regression() Runs a LPM of treatment status against all covariates (treatment~X'B).

Description

Usage

Value

Arguments

Details

Examples