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Type: Package
Title: Copula-Based Sensitivity Analysis for Observational Causal Inference
Version: 0.1.0
Description: Implements the copula-based sensitivity analysis method, as discussed in Copula-based Sensitivity Analysis for Multi-Treatment Causal Inference with Unobserved Confounding <doi:10.48550/arXiv.2102.09412>, with Gaussian copula adopted in particular.
URL: https://github.com/JiajingZ/CopSens
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
Depends: R (≥ 3.6.0)
Imports: pcaMethods (≥ 1.78.0), CVXR (≥ 1.0), MASS (≥ 7.3-53), ggplot2 (≥ 3.3.3), stats (≥ 4.0.3), dplyr (≥ 1.0.3), tidyr (≥ 1.1.2), tidyverse (≥ 1.3.0)
NeedsCompilation: no
Packaged: 2022-05-11 06:23:54 UTC; jiajing
Author: Jiajing Zheng [aut, cre], Alexander Franks [aut], Alex D'Amour [ctb]
Maintainer: Jiajing Zheng <carolinez6850@gmail.com>
Repository: CRAN
Date/Publication: 2022-05-12 08:30:02 UTC

Dataset with Gaussian Treatments and Binary Outcomes

Description

A dataset containing Gaussian treatments and binary outcomes of 10,000 observations.

Usage

GaussianT_BinaryY

Format

A data frame with eleven variables: one binary outcome, y, and ten Gaussian treatments, t1, t2, ..., t10.

Source

For data generating process, see data-raw/Data_Generation.R.

Dataset with Gaussian Treatments and Outcomes

Description

A dataset containing Gaussian treatments and outcomes of 10,000 observations.

Usage

GaussianT_GaussianY

Format

A data frame with eleven variables: one Gaussian outcome, y, and ten Gaussian treatments, t1, t2, ..., t10.

Source

For data generating process, see data-raw/Data_Generation.R.

Calibration for Binary Outcomes

Description

Calibrates the naive estimates to account for unobserved confounding when outcome variables are binary. The calibration can be done with user-specific sensitivity parameter or with our pre-provided calibration methods, the worst-case calibration for a single contrast or multivariate calibration for multiple contrasts.

Usage

bcalibrate(
  y,
  tr,
  t,
  gamma,
  R2 = NULL,
  mu_y_t = NULL,
  mu_u_tr = NULL,
  mu_u_t = NULL,
  cov_u_t = NULL,
  nU = NULL,
  nsim = 4000,
  ...
)

Arguments

y

data.frame, matrix or vector. Binary outcome variable.

tr

data.frame. Treatment variables with rows corresponding to observations and columns to variables.

t

data.frame. Treatment arms of interest. May contain a single or multiple treatments in rows.

gamma

a vector specifying the direction of sensitivity parameters.

R2

an optional scalar or vector specifying the proportion of residual variance in outcome given the treatment that can be explained by confounders, which determines the magnitude of sensitivity parameters.

mu_y_t

an optional scalar or vector that contains naive estimates of treatment effects ignoring confounding.

mu_u_tr

an optional matrix of conditional confounder means for all observed treatments with latent variables in columns.

mu_u_t

an optional matrix of conditional confounder means for treatments of interest with latent variables in columns.

cov_u_t

an optional covariance matrix of confounders conditional on treatments.

nU

Number of latent confounders to consider.

nsim

an optional scalar specifying the number of sample draws.

...

further arguments passed to kEstimateor pca.

Value

A data.frame with naive and calibrated estimates of population average outcome receiving treatment t.

Examples


# load the example data #
y <- GaussianT_BinaryY$y
tr <- subset(GaussianT_BinaryY, select = -c(y))
t1 <- tr[1:5,]
t2 <- rep(0, times = ncol(tr))
# calibration #
est_b <- bcalibrate(y = y, tr = tr, t = rbind(t1, t2),
                    nU = 3, gamma = c(1.27, -0.28, 0),
                    R2 = c(0.2, 0.7))
est_b_rr <- list(est_df = est_b$est_df[1:5,] / as.numeric(est_b$est_df[6,]),
                 R2 = c(0.2, 0.7))
plot_estimates(est_b_rr)

Calculate Robustness Value When Executing Worstcase Calibration

Description

Calculate Robustness Value When Executing Worstcase Calibration

Usage

cal_rv(
  y,
  tr,
  t1,
  t2,
  mu_y_dt = NULL,
  sigma_y_t = NULL,
  mu_u_dt = NULL,
  cov_u_t = NULL,
  nU = NULL,
  ...
)

Arguments

y

data.frame, matrix or vector. Gaussian outcome variable.

tr

data.frame. Treatment variables with rows corresponding to observations and columns to variables.

t1

data.frame. First treatment arms of interest. May contain a single or multiple treatments in rows.

t2

data.frame. Second treatment arms of interest, which has same number of row as t1.

mu_y_dt

an optional scalar or vector that contains naive estimates of treatment effects ignoring confounding.

sigma_y_t

an optional scalar of the standard deviation of outcome conditional on treatments.

mu_u_dt

an optional matrix of difference in conditional confounder means, E(U \mid t1) - E(U \mid t2), with latent variables in columns.

cov_u_t

an optional covariance matrix of confounders conditional on treatments.

nU

Number of latent confounders to consider.

...

further arguments passed to kEstimate, pca

Value

A numeric vector with elements being the robustness value or NA if the ignorance region doesn't contains 0 for each contrast of interest.

Examples


# load the example data #
y <- GaussianT_GaussianY$y
tr <- subset(GaussianT_GaussianY, select = -c(y))
# calculate robustness value #
cal_rv(y = y, tr = tr, t1 = tr[1:2,], t2 = tr[3:4,])

Calibrate Estimate of Intervention Mean for Binary Outcome

Description

Calibrate Estimate of Intervention Mean for Binary Outcome

Usage

cali_mean_ybinary_algm(i, gamma, mu_u_tr, mu_u_t, mu_y_t, nsim = 4000)

Arguments

i

Observation index.

gamma

Scalar or vector specifying the sensitivity parameters.

mu_u_tr

Matrix of conditional confounder means for all observed treatments with latent variables in columns.

mu_u_t

Matrix of conditional confounder means for treatments of interest with latent variables in columns.

mu_y_t

Scalar or vector that contains naive estimates of treatment effects ignoring confounding.

nsim

Number of simulation sample draws.

Value

Scalar of calibrated intervention mean.

Calibration for Gaussian Outcomes

Description

Calibrates the naive estimates to account for unobserved confounding when outcome variables are Gaussian. The calibration can be done with user-specific sensitivity parameters or with our pre-provided calibration methods, the worst-case calibration for a single contrast or multivariate calibration for multiple contrasts.

Usage

gcalibrate(
  y,
  tr,
  t1,
  t2,
  calitype = c("worstcase", "multicali", "null"),
  mu_y_dt = NULL,
  sigma_y_t = NULL,
  mu_u_dt = NULL,
  cov_u_t = NULL,
  nU = NULL,
  R2 = 1,
  gamma = NULL,
  R2_constr = 1,
  nc_index = NULL,
  ...
)

Arguments

y

data.frame, matrix or vector. Gaussian outcome variable.

tr

data.frame. Treatment variables with rows corresponding to observations and columns to variables.

t1

data.frame. First treatment arms of interest. May contain a single or multiple treatments in rows.

t2

data.frame. Second treatment arms of interest, which has same number of row as t1.

calitype

character. The calibration method to be applied. Can be one of:
"worstcase" - apply worst-case calibration when considering a single contrast.
"multicali" - apply mutlivariate calibration when considering multiple contrasts.
"null" - apply calibration with user-specified sensitivity parameter, \gamma.

mu_y_dt

an optional scalar or vector that contains naive estimates of treatment effects ignoring confounding.

sigma_y_t

an optional scalar of the standard deviation of outcome conditional on treatments.

mu_u_dt

an optional matrix of difference in conditional confounder means, E(U \mid t1) - E(U \mid t2), with latent variables in columns.

cov_u_t

an optional covariance matrix of confounders conditional on treatments.

nU

Number of latent confounders to consider.

R2

an optional scalar or vector specifying the proportion of residual variance in outcome given the treatment that can be explained by confounders.

gamma

sensitivity parameter vector. Must be given when calitype = "null".

R2_constr

an optional scalar or vector specifying the upper limit constraint on R^2 . By default, R2_constr = 1.

nc_index

an optional vector containing indexes of negative control treatments. If not NULL, worstcase calibration will be executed with constraints imposed by negative control treatments.

...

further arguments passed to kEstimate, pca or get_opt_gamma.

Value

gcalibrate returns a list containing the following components:

est_df

a data.frame with naive and calibrated estimates of average treatment effects.

R2

a vector of R^2 with elements corresponding to columns of est_df.

gamma

a matrix returned when calitype = "multicali" or "worstcase". If calitype = "multicali", optimized gamma are in columns, respectively resulting in estimates in columns of est_df. If calitype = "worstcase", gamma are in rows, which respectively lead to the worstcase ignorance region with R^2=1 for each contrast of interest.

rv

a numeric vector returned when calitype = "worstcase", with elements being the robustness value or NA if the ignorance region doesn't contains 0 for each contrast of interest.

Examples

# load the example data #
y <- GaussianT_GaussianY$y
tr <- subset(GaussianT_GaussianY, select = -c(y))

# worst-case calibration #
t1 <- data.frame(diag(ncol(tr)))
t2 <- data.frame(matrix(0, nrow = ncol(tr), ncol = ncol(tr)))
colnames(t1) = colnames(t2) <- colnames(tr)
est_g1 <- gcalibrate(y = y, tr = tr, t1 = t1, t2 = t2, nU = 3,
                     calitype = "worstcase", R2 = c(0.3, 1))
plot_estimates(est_g1)
# with negative conotrls #
est_g1_nc <- gcalibrate(y = y, tr = tr, t1 = t1, t2 = t2, nU = 3,
                        calitype = "worstcase", R2 = c(0.3, 1), nc_index = c(3, 6))
plot_estimates(est_g1_nc)


# multivariate calibration #
est_g2 <- gcalibrate(y = y, tr = tr, t1 = tr[1:10,], t2 = tr[11:20,], nU = 3,
                     calitype = "multicali", R2_constr = c(1, 0.15))
plot_estimates(est_g2)

# user-specified calibration #
est_g3 <- gcalibrate(y = y, tr = tr, t1 = tr[1:2,], t2 = tr[3:4,],
                     nU = 3, calitype = "null",
                     gamma = c(0.96, -0.29, 0), R2 = c(0.2, 0.6, 1))
plot_estimates(est_g3)
# apply gamma that maximizes the bias for the first contrast considered in est_g1 #
est_g4 <- gcalibrate(y = y, tr = tr, t1 = tr[1:2,], t2 = tr[3:4,],
                     nU = 3, calitype = "null",
                     gamma = est_g1$gamma[1,], R2 = c(0.2, 0.6, 1))
plot_estimates(est_g4)

Obtain Optimized Sensitivity Parameters Using Multivariate Calibration Criterion

Description

Obtain Optimized Sensitivity Parameters Using Multivariate Calibration Criterion

Usage

get_opt_gamma(
  mu_y_dt,
  mu_u_dt,
  cov_u_t,
  sigma_y_t,
  R2_constr = 1,
  normtype = "L2",
  ...
)

Arguments

mu_y_dt

Scalar or vector that contains naive estimates of treatment effects ignoring confounding.

mu_u_dt

Matrix of difference in conditional confounder means, E(U \mid t1) - E(U \mid t2), with latent variables in columns.

cov_u_t

Covariance matrix of confounders conditional on treatments.

sigma_y_t

Scalar of the standard deviation of outcome conditional on treatments.

R2_constr

an optional scalar or vector specifying the upper limit constraint on R^2 . By default, R2_constr = 1.

normtype

character. Optional function m for the multivariate calibration criterion. By default, the L2 norm will be applied.
"L1" - apply the L1 norm, sum(abs(x)).
"L2" - apply the L2 norm, sqrt(sum(x^2)).
"Inf" - apply the infinity norm, max(abs(x)).

...

further arguments passed to solve

Value

Optimized sensitivity parameters.

Estimates of genes' effects on mice body weight using null treatments approach from Miao et al. (2020)

Description

The dataset consists of estimates of treatment effects of 17 genes, which are likely to affect mouse weight, by using the null treatments approach from Miao et al. (2020), assuming that at least half of the confounded treatments have no causal effect on the outcome.

Usage

mice_est_nulltr

Format

A data frame with 17 rows and 6 variables:

esti

mean estimates of genes' treatment effects on mouse body weight

X2.5.

2.5% percentile of the estimates of genes' treatment effects on mouse body weight

X97.5.

97.5% percentile of the estimates of genes' treatment effects on mouse body weight

X5.

5% percentile of the estimates of genes' treatment effects on mouse body weight

X95.

95% percentile of the estimates of genes' treatment effects on mouse body weight

signif

significance

Source

https://arxiv.org/abs/2011.04504

Body weight and gene expressions of 287 mice

Description

A dataset are collected from 287 mice, including the body weight, 37 gene expressions, and 5 single nucleotide polymorphisms.

Usage

micedata

Format

A data frame with forty-three variables: the mice body weight, y, 5 single nucleotide polymorphisms, rs3663003, rs4136518, rs3694833, rs4231406, rs3661189, and the rest are thirty-seven genes.

Source

https://arxiv.org/abs/2011.04504

Visualize Estimates of Treatment Effects

Description

Visualize Estimates of Treatment Effects

Usage

plot_estimates(est, show_rv = TRUE, order = "naive", labels = NULL, ...)

Arguments

est

an return object from gcalibrate or bcalibrate, or data.frame containing estimates of treatment effects with estimates' type in columns and contrasts of interest in rows.

show_rv

logical. Whether robustness values should be printed in the plot or not? Available only for the "worstcase" calibration.

order

character. The type of order used to plot treatment effects from left to right. Can be one of the following:
"naive" - order by the naive estimate from smallest to largest. "worstcase" - place all treatments with negative robust effects on the left, with positive robust effects on the right, and all sensitive ones in the middle. Within the negative robust group, order treatments by the upper bound of the worst-case ignorance region from smallest to largest; within the positive robust group, order treatments by the lower bound of the worst-case ignorance region from smallest to largest; and within the sensitive group, order by the naive estimate from smallest to largest.

labels

character. Labels of treatments.

...

further arguments passed to theme

Value

A graph plotting ignorance regions of the causal estimands of interest.

Note

For examples, please refer to bcalibrate or gcalibrate

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.