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An all-in-one DAG-driven robustness check. Generate publication-quality reports that classify variables by causal role, compare the significance of DAG-derived models, and explicitly target estimands.
DAGassist does: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(dag = dag_model,
formula = feols(Y ~ X + M + C + Z + A + B, data = df),
estimand = c("SATE", "SACDE")
)
#> 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 | Minimal 1 (SATE) | Canonical | Canonical (SATE) | Raw (SACDE) | Weighted (SACDE) |
#> +==========+===========+===========+==================+===========+==================+=============+==================+
#> | X | 0.452*** | 1.256*** | 1.084*** | 1.256*** | 1.097*** | 0.719*** | 0.620*** |
#> +----------+-----------+-----------+------------------+-----------+------------------+-------------+------------------+
#> | | (0.032) | (0.027) | (0.018) | (0.026) | (0.018) | (0.023) | (0.037) |
#> +----------+-----------+-----------+------------------+-----------+------------------+-------------+------------------+
#> | M | 0.514*** | | | | | | |
#> +----------+-----------+-----------+------------------+-----------+------------------+-------------+------------------+
#> | | (0.021) | | | | | | |
#> +----------+-----------+-----------+------------------+-----------+------------------+-------------+------------------+
#> | C | 0.343*** | | | | | | |
#> +----------+-----------+-----------+------------------+-----------+------------------+-------------+------------------+
#> | | (0.019) | | | | | | |
#> +----------+-----------+-----------+------------------+-----------+------------------+-------------+------------------+
#> | Z | 0.249*** | 0.311*** | | 0.309*** | | 0.294*** | 0.440*** |
#> +----------+-----------+-----------+------------------+-----------+------------------+-------------+------------------+
#> | | (0.027) | (0.034) | | (0.033) | | (0.029) | (0.043) |
#> +----------+-----------+-----------+------------------+-----------+------------------+-------------+------------------+
#> | A | 0.152*** | | | 0.187*** | | 0.180*** | 0.188*** |
#> +----------+-----------+-----------+------------------+-----------+------------------+-------------+------------------+
#> | | (0.021) | | | (0.026) | | (0.023) | (0.036) |
#> +----------+-----------+-----------+------------------+-----------+------------------+-------------+------------------+
#> | B | -0.069*** | | | -0.057* | | -0.078*** | -0.099** |
#> +----------+-----------+-----------+------------------+-----------+------------------+-------------+------------------+
#> | | (0.021) | | | (0.026) | | (0.023) | (0.038) |
#> +----------+-----------+-----------+------------------+-----------+------------------+-------------+------------------+
#> | Num.Obs. | 2000 | 2000 | 2000 | 2000 | 2000 | 2000 | 2000 |
#> +----------+-----------+-----------+------------------+-----------+------------------+-------------+------------------+
#> | R2 | 0.818 | 0.706 | 0.655 | 0.714 | 0.664 | | |
#> +==========+===========+===========+==================+===========+==================+=============+==================+
#> | + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001 |
#> +==========+===========+===========+==================+===========+==================+=============+==================+
#>
#> Weight diagnostics:
#> legend: w range reports the min-max weights by group; ESS is kish effective sample size.
#> Minimal 1 (SATE): w range=0.024..371.8 | ESS (weighted)=56.15 [LOW_ESS,EXTREME_W]
#> Canonical (SATE): w range=0.02283..339.7 | ESS (weighted)=64.48 [LOW_ESS,EXTREME_W]
#>
#> 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 outcomeOptionally, users can generate visual output via dotwhisker plots:
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.
Health stats visible at Monitor.