Description Usage Arguments Value Author(s) See Also Examples

Create cutoff, then bootstrap or simulate the sampling distribution of participants that likely received the treatment (ATE among "compliers" and "non-compliers").

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`dataframe` |
Dataframe from which we will estimate our regression model. |

`formula` |
Symbolic representation of the model to be estimated. This is written in "typical" R language (i.e. y ~ x1 + x2), such that y is the outcome variable and x1 and x2 are the predictors. |

`similarity_measures` |
Vector(s) from dataframe that contains the similarity measures to be used as weights. Possible values for measure_type = c("osa", "lv", "dl", "hamming", "lcs", "qgram", "cosine", "jaccard", "jw", "soundex"). Default is "jaccard". |

`type_model` |
Statistical model to estimate. Currently support OLS and logistic ("ls", "logit"). |

`k` |
The penalty that you want to set for down-weighting inattentive respondents. Lower levels of k down-weight low attention participants more severely. |

`bounds` |
Minimum and maximum of uniform distribution we should draw cutoff values between. |

Dataframe of all estimated marginal effects that make up the sampling distribution for participants that did and did not receive the treatment.

Jeffrey Ziegler (<jeffrey.ziegler[at]emory.edu>)

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