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

Estimate average treatment effects using two-stage least squares with a binary instrument and binary treatment and weighting with inverse complier scores (probabilities of compliance). Optionally, bootstrap the entire estimation process for the purpose of hypothesis testing and constructing confidence intervals.

1 2 3 4 5 6 7 8 9 |

`D` |
Binary treatment of interest. |

`X` |
Matrix of covariates for two-stage least squares. Add a constant if desired (see examples). |

`Y` |
Outcome. |

`Z` |
Binary instrument. |

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

`weights` |
Observation weights. |

`R` |
Number of bootstrap replicates. |

`estimand` |
Whether to estimate average treatment effect (default) or average treatment effect on the treated. |

`min.prob.quantile` |
Compliance scores are truncated to this quantile of positive compliance scores. |

`min.prob` |
Compliance scores are truncated to this value. |

`...` |
Additional arguments to |

If R = 0 or icsw.tsls.fit is called directly, a model fit,
as described in `lm.wfit`

.

If R > 0, a list with elements

`fitted.model` |
A model fit, as returned by |

`coefs.boot` |
p by R matrix of model coefficients for each of R bootstrap replicates. |

`coefs.se.boot` |
Vector of standard deviations of coefficients under bootstrap resampling (i.e., bootstrap standard errors). |

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*.

`compliance.score`

for calculating compliance scores used in example.

`tsls.wfit`

for regression via 2SLS with weights.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 | ```
# Load example dataset, see help(FoxDebate) for details
data(FoxDebate)
# Ipw reweighting step Aronow and Carnegie (2013) use for missing data
covmat <- with(FoxDebate, cbind(partyid, pnintst, watchnat, educad, readnews, gender,
income, white))
# IPW reweighting step Aronow and Carnegie (2013) use for missing data
Ymis <- is.na(FoxDebate[, "infopro"])
IPWweight <- 1 / (1 - predict(glm(Ymis ~ covmat, family = binomial(link = "probit")),
type = "response"))
IPWweight[Ymis] <- 0
N <- length(FoxDebate[, "infopro"])
alpha <- 0.275
# Compute the ATE of watching the Fox Debate on knowledge . This replicates the
# ATE from column 2 of Table 1 in Aronow and Carnegie (2013)
icsw.out <- with(FoxDebate, icsw.tsls(D = watchpro, X = cbind(1, covmat),
Y = infopro, Z = conditn, W = covmat,
min.prob.quantile = 1 / (N^alpha),
weights = IPWweight))
round(icsw.out$coefficients["D"], 2)
# Example with bootstrap (this takes awhile!)
icsw.out <- with(FoxDebate, icsw.tsls(D = watchpro, X = cbind(1, covmat),
Y = infopro, Z = conditn, W = covmat,
min.prob.quantile = 1 / (N^alpha),
weights = IPWweight, R = 1000))
# Display vector of coefficients
icsw.out$coefficients
# Display vector of (bootstrapped) SEs
icsw.out$coefs.se.boot
``` |

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