fciR

fciR is a companion package to the book Fundamentals of Causal Inference With R by Babette A. Brumback, CRC Press 2022. It is important to remember that this package is not for commercial use as the functions have not been designed specifically to be to be efficient, robust or with error checks. This package is a learning tool, not a working tool.

Installation

You can install the development version of fciR from GitHub with:

# install.packages("devtools")
devtools::install_github("FrankLef/fciR")

Aknowledgement

All my thanks to the author, Ms Babette Brumback, for her patience and kindness in answering my queries and for all her work in creating this so useful book.

Overview

The package includes functions replacing the ones appearing in Fundamentals of Causal Inference to provide improved documentations, naming conventions and helper functions for plots, tables etc.

The relevant functions and their correspondence in the book are listed in the following table. It is important to note that all boostrapping code are replaced by fciR::boot_est.

Reference	Book	fciR	Description
Section 2.4, p. 33	lmodboot	prob_lmod	Estimate the sampling distribution
Section 2.4, p. 33	lmodboot	prob_lmod_td	Estimate the sampling distribution using tidyverse
Section 2.4, p. 34	lmodboot	boot_est	The bootstrapping code section is replaced by this one
Section 3.3, p. 46	bootu	meas_effect_uncond	Estimate unconditional association measures
Section 3.3, p. 50	lmodboot	meas_effect_uncond	Estimate conditional association measures
Section 4.1, p. 60	bootinside	meas_effect_modif	Estimate effect measure with modifications
Section 6.1, p. 101	stand	backdr_out_npr	backdoor method via outcome model using non parametric regression
Section 6.1.1, p. 106	standatt	backdr_out_npr	same as backdr_out_npr but with att=TRUE
Section 6.1.2, p. 111	standout	backdr_out	backdoor via outcome model using parametric method
Section 6.1.2, p. 112	standout	backdr_out	Same as previous one but using different parameters
Section 6.2, p. 114	mk.mordat	backdr_exp_bb	Standardization via exposure modeling
Section 6.2, p. 115	mordat.out	backdr_out	Standardization via outcome modeling
Section 6.2.1, p. 116	attsem	backdr_out	Standardization via outcome modeling with att=TRUE
Section 6.2.2, p. 118	standexp	backdr_exp	Standardization via parametric exposure model
Section 6.2.2, p. 119	exp	backdr_exp_gee	Standardization via parametric exposure model with geepack::geeglm
Section 6.2.2, p. 119	standep	backdr_exp	Same as above for p. 118
Section 6.2.2, p. 120	prop	prop_mod	Fit the propensity score model
Section 6.3, p. 125	badstanddr	backdr_dr_bad	Misspecified doubly robust standardization
Section 6.3, p. 127	simdr	mc_standdr	Monte-Carlo simulation investigating small-sample robustness
Section 7.2, p. 141	didlinear	did_linear	Difference-in-Differences estimator with linear model
Section 7.2, p. 141	didloglinear	did_loglinear	Difference-in-Differences estimator with loglinear model
Section 7.2, p. 142	didlogistic	did_logistic	Difference-in-Differences estimator with logistic model
Section 7.2, p. 142	bootdid	boot_est	Same bootstrapping function used everywhere else in the package
Section 8.3, p. 153	frontdoor	frontdr_np	Front-door method non-parametric
Section 9.3, p. 164	iv	instr_vars	ITT, CACE and ATT from instrument variables
Section 9.3, p. 167	ividentity	instr_linear	Estimate effect using instrument variables
Section 9.3, p. 167	ivlog	instr_loglinear	Estimate effect using instrument variables via logarithmic fit
Section 9.3, p. 168	ivlogit	instr_logistic	Estimate effect using instrument variables via logistic fit
Section 10.2, p. 179	estand	backdr_out	Outcome-model standardization compared to propensity score
Section 10.3, p. 181	equartiles	prop_quant	Stratifying on the quantiles of the propensity score
Section 11.2, p. 190	precision	precision_eff	Compute precision efficiency.
Section 11.2, p. 190	bootprecision	precision_stats	Compute stats on precision efficiency.
Section 12.3, p. 201	mediation	mediation_np	Estimate non-parametric mediation effect.
Section 12.3, p. 202	nonparamediation	mediation	Estimate mediation effect with parametric assumptions.
Section 13.1, p. 211	msm	time_msm	Estimate using marginal structural models.
Section 13.2, p. 214	snmm	time_snmm	Estimate using structural mested mean models.
Section 13.3, p. 216	mkcogtab.r	time_odtr_prop	Optimal dynamic treatment regimes, step 1.
Section 13.3, p. 217	A2opt.r	time_odtr_optA2	Optimal dynamic treatment regimes, step 2.
Section 13.3, p. 217	A2opt.r	time_odtr_optA2	Optimal dynamic treatment regimes, step 3.
Section 13.3, p. 218	A1opt.r	time_odtr_optA1A2	Optimal dynamic treatment regimes, step 4.
Section 13.3, p. 219	optimal.r	time_odtr_optimal	Optimal dynamic treatment regimes, step 4.

Packages

The packages used by fciR include the usual great ones

Package	Description
rlang	Core R functions with tidyverse
dplyr	Data wrangling
tidyr	Create tidy data
tidyselect	Select from a set of string
purrr	Functional programming toolkit
ggplot2	Nice plots

and several packages used for more specialized tasks

Package	Description
boot	Boostrapping
rsample	Boostrapping and jackknife
broom	Extract information from models
MonteCarlo	Monte Carlo simulations
formulaic	Dynamic creation and quality checks of formula
gee	Generalized estimation equation solver
geepack	Generalized estimation equation package
AER	Applied econometrics with R
Matching	Multivariate and propensity score matching