Type: Package
Title: Causal Inference in Case-Control and Case-Population Studies
Version: 0.3.0
Description: Estimation and inference methods for causal relative and attributable risk in case-control and case-population studies under the monotone treatment response and monotone treatment selection assumptions. For more details, see the paper by Jun and Lee (2023), "Causal Inference under Outcome-Based Sampling with Monotonicity Assumptions," <doi:10.48550/arXiv.2004.08318>, accepted for publication in Journal of Business & Economic Statistics.
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.2.3
Imports: stats, glmnet
Suggests: knitr, rmarkdown, testthat, MASS, splines, Matrix
VignetteBuilder: knitr
Depends: R (≥ 2.10)
URL: https://github.com/sokbae/ciccr/
BugReports: https://github.com/sokbae/ciccr/issues
NeedsCompilation: no
Packaged: 2023-10-20 16:29:09 UTC; sokbaelee
Author: Sung Jae Jun [aut], Sokbae Lee [aut, cre]
Maintainer: Sokbae Lee <sl3841@columbia.edu>
Repository: CRAN
Date/Publication: 2023-10-20 21:30:02 UTC

ciccr: Causal Inference in Case-Control and Case-Population Studies

Description

Estimation and inference methods for causal relative and attributable risk in case-control and case-population studies under the monotone treatment response and monotone treatment selection assumptions. For more details, see the paper by Jun and Lee (2023), "Causal Inference under Outcome-Based Sampling with Monotonicity Assumptions," arXiv:2004.08318 [econ.EM], accepted for publication in Journal of Business & Economic Statistics.

Author(s)

Maintainer: Sokbae Lee sl3841@columbia.edu

Authors:

See Also

Useful links:

Average Adjusted Association

Description

Averages the log odds ratio using prospective or retrospective high-dimensional logistic regression

Usage

AAA_DML(y, t, x, type = "pro", k = 10)

Arguments

y

n-dimensional vector of binary outcomes

t

n-dimensional vector of binary treatments

x

n by d matrix of covariates

type

'pro' if the average is based on prospective regression; 'retro' if it is based on prospective regression (default = 'pro')

k

number of folds in k-fold partition (default = 10)

Value

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

est

a scalar estimate

se

standard error

References

Jun, S.J. and Lee, S. (2020). Causal Inference under Outcome-Based Sampling with Monotonicity Assumptions. https://arxiv.org/abs/2004.08318.

Examples

# use the ACS dataset included in the package
  y = ciccr::ACS$topincome
  t = ciccr::ACS$baplus
  age = ciccr::ACS$age
  x = splines::bs(age, df=6) # b-splines for age

  results = AAA_DML(y, t, x, 'pro', k=2)

ACS

Description

Sample extracted from American Community Survey (ACS) 2018, restricted to white males residing in California with at least a bachelor's degree. The sample is composed of 17,816 individuals whose age is restricted to be between 25 and 70.

Usage

ACS

Format

A data frame with 17,816 rows and 4 variables:

age

age, in years

ind

industry code, in four digits

baplus

1 if a respondent has a master’s degree, a professional degree, or a doctoral degree; 0 otherwise

topincome

1 if income is top-coded; 0 otherwise

Source

https://usa.ipums.org/usa/

ACS_CC

Description

A case-control sample extracted from American Community Survey (ACS) 2018, restricted to white males residing in California with at least a bachelor's degree. The original ACS dataset is not from case-control sampling, but this case-control sample is obtained by the following procedure. The case sample is composed of 921 individuals whose income is top-coded. The control sample of equal size is randomly drawn without replacement from the pool of individuals whose income is not top-coded. Age is restricted to be between 25 and 70.

Usage

ACS_CC

Format

A data frame with 1842 rows and 4 variables:

age

age, in years

ind

industry code, in four digits

baplus

1 if a respondent has a master’s degree, a professional degree, or a doctoral degree; 0 otherwise

topincome

1 if income is top-coded; 0 otherwise

Source

https://usa.ipums.org/usa/

ACS_CP

Description

A case-population sample extracted from American Community Survey (ACS) 2018, restricted to white males residing in California with at least a bachelor's degree. The original ACS dataset is not from case-population sampling, but this case-population sample is obtained by the following procedure. The case sample is composed of 921 individuals whose income is top-coded. The control sample of equal size is randomly drawn without replacement from all observations and its top-coded status is coded missing. Age is restricted to be between 25 and 70.

Usage

ACS_CP

Format

A data frame with 1842 rows and 4 variables:

age

age, in years

ind

industry code, in four digits

baplus

1 if a respondent has a master’s degree, a professional degree, or a doctoral degree; 0 otherwise

topincome

1 if an observation belongs to the case sample; NA otherwise

Source

https://usa.ipums.org/usa/

DZ_CC

Description

Case-control sample extracted from Delavande and Zafar (2019). The sample is composed of 689 students who attended either "Very Selective University" (VSU) or "Selective University" (SU).

Usage

DZ_CC

Format

A data frame with 689 rows and 5 variables:

parent_ba

indicator: at least one college-educated parent

private_school

indicator: attended private school before unversity

parent_inc

parents' monthly income (in 1000s Rs)

case_sample

indicator: attended "Very Selective University" (VSU)

control_sample

indicator: attended the other simply "Selective University" (SU)

Source

https://www.journals.uchicago.edu/doi/suppl/10.1086/701808/suppl_file/2014399data.zip

References

Delavande, A. and Zafar, B. (2019). University Choice: The Role of Expected Earnings, Non-Pecuniary Outcomes, and Financial Constraints. Journal of Political Economy 127(5), 2343-2393.

FG

Description

Dataset from Fang and Gong (2017,2020). The original dataset in Fang and Gong (2017) is updated in Fang and Gong (2020) after Matsumoto (2020) pointed out data and coding errors in the original work. We use the updated version of the dataset. The sample is composed of 78,165 physicians who billed at least 20 hours per week.

Usage

FG

Format

A data frame with 78,165 rows and 5 variables:

male

indicator: physician is male

isMD

indicator: physician has a MD degree

experYear

experience in years

flag

indicator: physician billed for more than 100 hours per week

smallPractice

indicator: number of group practice members less than 6

Source

Fang, H. and Gong, Q. (2020) Data and Code for: Detecting Potential Overbilling in Medicare Reimbursement via Hours Worked: Reply. Nashville, TN: American Economic Association [publisher]. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2020-11-23. doi:10.3886/E119192V1

References

Fang, H. and Gong, Q. (2017). Detecting Potential Overbilling in Medicare Reimbursement via Hours Worked. American Economic Review, 107(2), 562-91.

Matsumoto, B. (2020). Detecting Potential Overbilling in Medicare Reimbursement via Hours Worked: Comment. American Economic Review, 110(12), 3991-4003.

Fang, H. and Gong, Q. (2020). Detecting Potential Overbilling in Medicare Reimbursement via Hours Worked: Reply. American Economic Review, 110(12): 4004-10.

FG_CC

Description

Case-control sample extracted from Fang and Gong (2020). The case-control sample is extracted from Fang and Gong (2020) by the following procedure. The case sample is composed of 2,261 flagged physicians (that is, those who billed for more than 100 hours per week). The control sample of equal size is randomly drawn without replacement from the pool of physicians who were never flagged. The sample is composed of 4,522 physicians who billed at least 20 hours per week.

Usage

FG_CC

Format

A data frame with 4,522 rows and 5 variables:

male

indicator: physician is male

isMD

indicator: physician has a MD degree

experYear

experience in years

flag

indicator: physician billed for more than 100 hours per week

smallPractice

indicator: number of group practice members less than 6

Source

Fang, H. and Gong, Q. (2020) Data and Code for: Detecting Potential Overbilling in Medicare Reimbursement via Hours Worked: Reply. Nashville, TN: American Economic Association [publisher]. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2020-11-23. doi:10.3886/E119192V1

References

Fang, H. and Gong, Q. (2017). Detecting Potential Overbilling in Medicare Reimbursement via Hours Worked. American Economic Review, 107(2), 562-91.

Matsumoto, B. (2020). Detecting Potential Overbilling in Medicare Reimbursement via Hours Worked: Comment. American Economic Review, 110(12), 3991-4003.

Fang, H. and Gong, Q. (2020). Detecting Potential Overbilling in Medicare Reimbursement via Hours Worked: Reply. American Economic Review, 110(12), 4004-10.

FG_CP

Description

Case-population sample extracted from Fang and Gong (2020). The case-population sample is extracted from Fang and Gong (2020) by the following procedure. The case sample is composed of 2,261 flagged physicians (that is, those who billed for more than 100 hours per week). The control sample of equal size is randomly drawn without replacement from all observations and its flagged status is coded missing. The sample is composed of 4,522 physicians who billed at least 20 hours per week.

Usage

FG_CP

Format

A data frame with 4,522 rows and 5 variables:

male

indicator: physician is male

isMD

indicator: physician has a MD degree

experYear

experience in years

flag

1 if an observation belongs to the case sample; NA otherwise

smallPractice

indicator: number of group practice members less than 6

Source

Fang, H. and Gong, Q. (2020) Data and Code for: Detecting Potential Overbilling in Medicare Reimbursement via Hours Worked: Reply. Nashville, TN: American Economic Association [publisher]. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2020-11-23. doi:10.3886/E119192V1

References

Fang, H. and Gong, Q. (2017). Detecting Potential Overbilling in Medicare Reimbursement via Hours Worked. American Economic Review, 107(2), 562-91.

Matsumoto, B. (2020). Detecting Potential Overbilling in Medicare Reimbursement via Hours Worked: Comment. American Economic Review, 110(12), 3991-4003.

Fang, H. and Gong, Q. (2020). Detecting Potential Overbilling in Medicare Reimbursement via Hours Worked: Reply. American Economic Review, 110(12): 4004-10.

An Average of the Upper Bound on Causal Attributable Risk

Description

Averages the upper bound on causal attributable risk using prospective and retrospective logistic regression models under the monotone treatment response (MTR) and monotone treatment selection (MTS) assumptions.

Usage

avg_AR_logit(
  y,
  t,
  x,
  sampling = "cc",
  p_upper = 1L,
  length = 21L,
  interaction = TRUE,
  eps = 1e-08
)

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')

p_upper

specified upper bound for the unknown true case probability (default = 1)

length

specified length of a sequence from 0 to p_upper (default = 21)

interaction

TRUE if there are interaction terms in the retrospective logistic model; FALSE if not (default = TRUE)

eps

a small constant that determines the trimming of the estimated probabilities. Specifically, the estimate probability is trimmed to be between eps and 1-eps (default = 1e-8).

Value

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

est

(length)-dimensional vector of the average of the upper bound of causal attributable risk

pseq

(length)-dimensional vector of a grid from 0 to p_upper

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 = avg_AR_logit(y, t, x, sampling = 'cc')

An Average of the Log Odds Ratio

Description

Averages the log odds ratio using retrospective logistic regression.

Usage

avg_RR_logit(y, t, x, w = "control")

Arguments

y

n-dimensional vector of binary outcomes

t

n-dimensional vector of binary treatments

x

n by d matrix of covariates

w

'case' if the average is conditional on the case sample; 'control' if it is conditional on the control sample; 'all' if it is based on the whole sample; default w = 'control'

Value

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

est

a scalar estimate of the weighted average of the log odds ratio using retrospective logistic regression

se

standard error

References

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

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
# use 'case' to condition on the distribution of covariates given y = 1
  results = avg_RR_logit(y, t, x, 'case')

Causal Inference on Attributable Risk

Description

Provides an upper bound on the average of attributable risk under the monotone treatment response (MTR) and monotone treatment selection (MTS) assumptions.

Usage

cicc_AR(
  y,
  t,
  x,
  sampling = "cc",
  p_upper = 1L,
  cov_prob = 0.95,
  length = 21L,
  interaction = TRUE,
  no_boot = 0L,
  eps = 1e-08
)

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')

p_upper

a specified upper bound for the unknown true case probability (default = 1)

cov_prob

coverage probability of a confidence interval (default = 0.95)

length

specified length of a sequence from 0 to p_upper (default = 21)

interaction

TRUE if there are interaction terms in the retrospective logistic model; FALSE if not (default = TRUE)

no_boot

number of bootstrap repetitions to compute the confidence intervals (default = 0)

eps

a small constant that determines the trimming of the estimated probabilities. Specifically, the estimate probability is trimmed to be between eps and 1-eps (default = 1e-8).

Value

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

est

(length)-dimensional vector of the upper bounds on the average of attributable risk

ci

(length)-dimensional vector of the upper ends of pointwise one-sided confidence intervals

pseq

(length)-dimensional vector of a grid from 0 to p_upper

cov_prob

the nominal coverage probability

return_code

status of existence of missing values in bootstrap replications

References

Jun, S.J. and Lee, S. (2020). 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_AR = cicc_AR(y, t, x, sampling = 'cc', no_boot = 100)

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)

Plotting Upper Bounds on Relative and Attributable Risk

Description

Plots upper bounds on relative and attributable risk

Usage

cicc_plot(
  results,
  parameter = "RR",
  sampling = "cc",
  save_plots = FALSE,
  file_name = Sys.Date(),
  plots_ctl = 0.3
)

Arguments

results

estimation results from either cicc_RR or cicc_AR

parameter

'RR' for relative risk; 'AR' for attributable risk (default = 'RR')

sampling

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

save_plots

TRUE if the plots are saved as pdf files; FALSE if not (default = FALSE)

file_name

the pdf file name to save the plots (default = Sys.Date())

plots_ctl

value to determine the topleft position of the legend in the figure a large value makes the legend far away from the confidence intervals (default = 0.3)

Value

A X-Y plot where the X axis shows the range of p from 0 to p_upper and the Y axis depicts both point estimates and the upper end point of the one-sided confidence intervals.

References

Jun, S.J. and Lee, S. (2020). Causal Inference under Outcome-Based Sampling with Monotonicity Assumptions. https://arxiv.org/abs/2004.08318.

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 = cicc_RR(y, t, x)
  cicc_plot(results)

Trimming the estimates to be strictly between 0 and 1

Description

Trimming the estimates to be strictly between 0 and 1

Usage

trim_pr(ps, eps = 1e-08)

Arguments

ps

n-dimensional vector of estimated probabilities

eps

a small constant that determines the trimming of the estimated probabilities. Specifically, the estimate probability is trimmed to be between eps and 1-eps (default = 1e-8).

Value

ps_tr

n-dimensional trimmed estimates