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An all-in-one DAG-driven robustness check. Classify variables by causal role, compute the smallest and largest permissible back-door adjustment sets, and compare the significance of models.
See the Quick
Tour vignette for a 10 minute start-to-finish guide on how to use
DAGassist to identify causal roles, create reports, and
interpret the results.
See the Making
Reports vignette for details on producing publication-quality
DAGassist reports in LaTex, Word,
Excel, and plain text.
See the Parameter
Guide vignette for examples of how to get the most out of
DAGassist.
See the Supported
Models vignette for documentation on what engines
DAGassist supports.
See the Ecosystem
Guide for how DAGassist fits in the R DAG ecosystem—and
a diagram of the packages it integrates with.
You can install DAGassist with:
install.packages("DAGassist")
library(DAGassist) Or you can install the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("grahamgoff/DAGassist")Simply provide a dagitty() object and a regression call
and DAGassist will create a report classifying variables by
causal role, and compare the specified regression to minimal and
canonical models.
DAGassist::DAGassist(dag = dag_model,
formula = feols(Y ~ X + M + C + Z + A + B, data = df))
#> DAGassist Report:
#>
#> Roles:
#> variable role Exp. Out. conf med col dOut dMed dCol dConfOn dConfOff NCT NCO
#> X exposure x
#> Y outcome x
#> Z confounder x
#> M mediator x
#> C collider x x x
#> A nco x
#> B nco x
#>
#> (!) Bad controls in your formula: {M, C}
#> Minimal controls 1: {Z}
#> Canonical controls: {A, B, Z}
#>
#> Formulas:
#> original: Y ~ X + M + C + Z + A + B
#>
#> Model comparison:
#>
#> +---+-----------+-----------+-----------+
#> | | Original | Minimal 1 | Canonical |
#> +===+===========+===========+===========+
#> | X | 0.452*** | 1.256*** | 1.256*** |
#> +---+-----------+-----------+-----------+
#> | | (0.032) | (0.027) | (0.026) |
#> +---+-----------+-----------+-----------+
#> | M | 0.514*** | | |
#> +---+-----------+-----------+-----------+
#> | | (0.021) | | |
#> +---+-----------+-----------+-----------+
#> | C | 0.343*** | | |
#> +---+-----------+-----------+-----------+
#> | | (0.019) | | |
#> +---+-----------+-----------+-----------+
#> | Z | 0.249*** | 0.311*** | 0.309*** |
#> +---+-----------+-----------+-----------+
#> | | (0.027) | (0.034) | (0.033) |
#> +---+-----------+-----------+-----------+
#> | A | 0.152*** | | 0.187*** |
#> +---+-----------+-----------+-----------+
#> | | (0.021) | | (0.026) |
#> +---+-----------+-----------+-----------+
#> | B | -0.069*** | | -0.057* |
#> +---+-----------+-----------+-----------+
#> | | (0.021) | | (0.026) |
#> +===+===========+===========+===========+
#> | + p < 0.1, * p < 0.05, ** p < 0.01, |
#> | *** p < 0.001 |
#> +===+===========+===========+===========+
#>
#> Roles legend: Exp. = exposure; Out. = outcome; CON = confounder; MED = mediator; COL = collider; dOut = descendant of outcome; dMed = descendant of mediator; dCol = descendant of collider; dConfOn = descendant of a confounder on a back-door path; dConfOff = descendant of a confounder off a back-door path; NCT = neutral control on treatment; NCO = neutral control on outcome
# note: this example uses a test DAG and dataset, which was created
# silently for the sake of brevity.Optionally, users can generate visual output via dotwhisker plots:
DAGassist::DAGassist(dag = dag_model,
formula = feols(Y ~ X + M + C + Z + A + B, data = df),
type = "dotwhisker")
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.