RCT {RCT}

Designing, random assigning and evaluating Randomized Control Trials

Description

RCT provides three important group of functions: a) functions for pre-processing the design of the RCT b) Functions for assigning treatment status and checking for balances c) Function for evaluating the impact of the RCT

Details

RCT helps you focus on the statistics of the randomized control trials, rather than the heavy programming lifting. RCT helps you in the whole process of designing and evaluating a RCT. 1. Clean and summarise the data in which you want to randomly assign treatment 2. Decide the share of observations that will go to control group 3. Decide which variables to use for strata building 4. Robust Random Assignment by strata/blocks 5 Impact evaluation of all y's and heterogeneities To lean more about RCT, start with the vignette: browseVignettes(package = "RCT")

RCT functions

treatment_assign: Robust treatment assign by strata/blocks

impact_eval: Automatized impact evaluation with heterogeneous treatment effects

balance_table: Balance tables for any length of covariates

balance_regression: LPM of treatment status against covariates with F-test

tau_min: Computation of the minimum detectable effect between control & treatment units

tau_min_probability: Computation of the minimum detectable effect between control & treatment units for dichotomous y-vars

summary_statistics: Summary statistics of all numeric columns in your data

ntile_label: Rank and divide observations in n groups, with label

Author(s)

Isidoro Garcia Urquieta, [e-mail]

References

Athey, Susan, and Guido W. Imbens (2017) "The Econometrics Randomized Experiments". Handbook of economic field experiments. https://arxiv.org/abs/1607.00698

See Also

Useful links: https://github.com/isidorogu/RCT Report bugs at https://github.com/isidorogu/RCT/issues