cicc_RR: Causal Inference on Relative Risk

View source: R/cicc_RR.R

cicc_RRR Documentation

Causal Inference on Relative Risk

Description

Provides upper bounds on the average of log relative risk under the monotone treatment response (MTR) and monotone treatment selection (MTS) assumptions.

Usage

cicc_RR(y, t, x, sampling = "cc", cov_prob = 0.95)

Arguments

y

n-dimensional vector of binary outcomes

t

n-dimensional vector of binary treatments

x

n by d matrix of covariates

sampling

'cc' for case-control sampling; 'cp' for case-population sampling; 'rs' for random sampling (default = 'cc')

cov_prob

coverage probability of a uniform confidence band (default = 0.95)

Value

An S3 object of type "ciccr". The object has the following elements:

est

estimates of the upper bounds on the average of log relative risk at p=0 and p=1

se

pointwise standard errors at p=0 and p=1

ci

the upper end points of the uniform confidence band at p=0 and p=1

pseq

two end points: p=0 and p=1

References

Jun, S.J. and Lee, S. (2023). Causal Inference under Outcome-Based Sampling with Monotonicity Assumptions. https://arxiv.org/abs/2004.08318.

Manski, C.F. (1997). Monotone Treatment Response. Econometrica, 65(6), 1311-1334.

Manski, C.F. and Pepper, J.V. (2000). Monotone Instrumental Variables: With an Application to the Returns to Schooling. Econometrica, 68(4), 997-1010.

Examples

# use the ACS_CC dataset included in the package.
  y = ciccr::ACS_CC$topincome
  t = ciccr::ACS_CC$baplus
  x = ciccr::ACS_CC$age
  results_RR = cicc_RR(y, t, x, sampling = 'cc', cov_prob = 0.95)


ciccr documentation built on Oct. 21, 2023, 1:08 a.m.