# Create a regression discontinuity design

Builds a design with sample from population of size `N`

. The average treatment effect local to the cutpoint is equal to `tau`

. It allows for specification of the order of the polynomial regression (`poly_reg_order`

), cutoff value on the running variable (`cutoff`

), and size of bandwidth around the cutoff (`bandwidth`

). By providing a vector of numbers to `control_coefs`

and `treatment_coefs`

, users can also specify polynomial regression coefficients that generate the expected control and treatment potential outcomes given the running variable.

regression_discontinuity_designer(N = 1000, tau = 0.15, outcome_sd = 0.1, cutoff = 0.5, bandwidth = 0.5, control_coefs = c(0.5, 0.5), treatment_coefs = c(-5, 1), poly_reg_order = 4, args_to_fix = NULL)

## Arguments

N | An integer. Size of population to sample from. |
---|---|

tau | A number. Difference in potential outcomes functions at the threshold. |

outcome_sd | A positive number. The standard deviation of the outcome. |

cutoff | A number in (0,1). Threshold on running variable beyond which units are treated. |

bandwidth | A number. The value of the bandwidth on both sides of the threshold from which to include units. |

control_coefs | A vector of numbers. Coefficients for polynomial regression function that generates control potential outcomes. Order of polynomial is equal to length. |

treatment_coefs | A vector of numbers. Coefficients for polynomial regression function that generates treatment potential outcomes. Order of polynomial is equal to length. |

poly_reg_order | Integer greater than or equal to 1. Order of the polynomial regression used to estimate the jump at the cutoff. |

args_to_fix | A character vector. Names of arguments to be args_to_fix in design. |

## Value

A regression discontinuity design.

## Details

See vignette online.

## Examples

# Generate a regression discontinuity design using default arguments: regression_discontinuity_design <- regression_discontinuity_designer()