Description Usage Arguments Details Value Note Author(s) References See Also Examples

Estimate probability that units are compliers using generalized linear models for probability of being a complier or always-taker and for being an always-taker conditional on being a complier or always-taker. These compliance scores can be used in inverse probability weighting to estimate average treatment effects. In the case of one-sided non-compliance, this estimation is considerably simpler.

1 2 3 4 |

`D` |
Binary treatment of interest. |

`Z` |
Binary instrument. |

`W` |
Matrix of covariates for compliance model. |

`weights` |
Observation weights. |

`link` |
Link function applied for linear models. Defaults to probit link function. |

`inv.link` |
Inverse link function (i.e., mean function) applied for linear models. Defaults to probit mean function. |

`genoud` |
Whether to use global optimization via genetic optimization from package |

`num.iter` |
Number of iterations of optimization routine. |

`one.sided` |
Whether non-compliance is one-sided (logical). When compliance is one-sided, the previous four arguments are ignored, and the compliance scores are estimated with probit regression. |

A unit *i* is a complier if *D_{i1} > D_{i0}*, where *D_{i1}* and
*D_{i0}* are the potential treatments for unit *i* when *Z* is set to
1 and 0. This is a latent (unobserved) characteristic of individual units, since
each unit is only observed with one value of *Z*.

By default this function uses genetic optimization via `genoud`

because
the loss function for the complier scores is not necessarily convex.

Vector of estimated probabilities of being a complier (i.e., compliance scores).

Requires `rgenoud`

package if `genoud`

= TRUE. Requires `minqa`

package if `genoud`

= FALSE.

Peter M. Aronow <peter.aronow@yale.edu>, Dean Eckles <icsw@deaneckles.com>, Kyle Peyton <kyle.peyton@yale.edu>

Peter M. Aronow and Allison Carnegie. (2013). Beyond LATE: Estimation of the average treatment effect with an instrumental variable. *Political Analysis*.

Used by `icsw.tsls`

.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
# Load example dataset, see help(FoxDebate) for details
data(FoxDebate)
# Matrix of covariates
covmat <- with(FoxDebate, cbind(partyid, pnintst, watchnat, educad, readnews,
gender, income, white))
# Estimate compliance scores with covariates, assuming (default)
# case of two-sided non-compliance
cscoreout <- with(FoxDebate, compliance.score(D = watchpro, Z = conditn,
W = covmat))
# Extract vector of estimated compliance scores
cscore <- cscoreout$C.score
summary(cscore)
``` |

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