Description Usage Arguments Value Examples

View source: R/estimate_Sid_bound.R

`estimate_Sid_bound`

estimates the partial identification bound
for each instance in the input dataset with a binary IV, observed
covariates, a binary treatment indicator, and a binary outcome according
to Siddique (2013, JASA).

1 | ```
estimate_Sid_bound(dt, method = "rf", nodesize = 5)
``` |

`dt` |
A dataframe whose first column is a binary IV 'Z', followed by q columns of observed covariates, followed by a binary treatment indicator 'A', and finally followed by a binary outcome 'Y'. The dataset has q+3 columns in total. |

`method` |
A character string indicator the method used to estimate each constituent conditional probability of the partial identification bound. Users can choose to fit multinomial regression by setting method = 'multinom', and random forest by setting method = 'rf'. |

`nodesize` |
Node size to be used in a random forest algorithm if method is set to 'rf'. The default value is set to 5. |

The original dataframe with two additional columns: L and U. L indicates the lower bound and U the upper bound as in Siddique 2013

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 | ```
attach(dt_Rouse)
# Construct an IV out of differential distance to two-year versus
# four-year college. Z = 1 if the subject lives not farther from
# a 4-year college compared to a 2-year college.
Z = (dist4yr <= dist2yr) + 0
# Treatment A = 1 if the subject attends a 4-year college and 0
# otherwise.
A = 1 - twoyr
# Outcome Y = 1 if the subject obtained a bachelor's degree
Y = (educ86 >= 16) + 0
# Prepare the dataset
dt = data.frame(Z, female, black, hispanic, bytest, dadsome,
dadcoll, momsome, momcoll, fincome, fincmiss, A, Y)
# Calculate the Siddique bound by estimating each constituent
# conditional probability p(Y = y, A = a | Z, X) with a random
# forest.
dt_with_Sid_bound_rf = estimate_Sid_bound(dt, method = 'rf', nodesize = 5)
# Calculate the Siddique bound by estimating each constituent
# conditional probability p(Y = y, A = a | Z, X) with a multinomial
# regression.
dt_with_Sid_bound_multinom = estimate_Sid_bound(dt, method = 'multinom')
``` |

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