check_dag.md
In performance: Assessment of Regression Models Performance

check_dag

Code
  print(dag)
Output
  # Check for correct adjustment sets
  - Outcome: y
  - Exposure: x

  Identification of direct and total effects

  Model is correctly specified.
  No adjustment needed to estimate the direct and total effect of `x` on `y`.

Code
  print(dag)
Output
  # Check for correct adjustment sets
  - Outcome: y
  - Exposure: x
  - Adjustment: b

  Identification of direct and total effects

  Model is correctly specified.
  All minimal sufficient adjustments to estimate the direct and total effect were done.

Code
  print(dag)
Output
  # Check for correct adjustment sets
  - Outcome: y
  - Exposure: x

  Identification of direct and total effects

  Incorrectly adjusted!
  To estimate the direct and total effect, at least adjust for `b`. Currently, the model does not adjust for any variables.

Code
  print(dag)
Output
  # Check for correct adjustment sets
  - Outcome: y
  - Exposure: x
  - Adjustment: c

  Identification of direct and total effects

  Incorrectly adjusted!
  To estimate the direct and total effect, at least adjust for `b` and `c`. Currently, the model only adjusts for `c`. You possibly also need to adjust for `b` to block biasing paths.

Code
  print(dag)
Output
  # Check for correct adjustment sets
  - Outcome: y
  - Exposure: x
  - Adjustment: c

  Identification of direct and total effects

  Incorrectly adjusted!
  To estimate the direct and total effect, at least adjust for `b` and `c`. Currently, the model only adjusts for `c`. You possibly also need to adjust for `b` to block biasing paths.

Code
  print(dag)
Output
  # Check for correct adjustment sets
  - Outcome: mpg
  - Exposure: wt
  - Adjustments: cyl, disp and gear

  Identification of direct and total effects

  Model is correctly specified.
  All minimal sufficient adjustments to estimate the direct and total effect were done.

check_dag, multiple adjustment sets

Code
  print(dag)
Output
  # Check for correct adjustment sets
  - Outcome: exam
  - Exposure: podcast

  Identification of direct and total effects

  Incorrectly adjusted!
  To estimate the direct and total effect, at least adjust for one of the following sets:
  - alertness, prepared
  - alertness, skills_course
  - mood, prepared
  - mood, skills_course.
  Currently, the model does not adjust for any variables.

Code
  print(dag)
Output
  # Check for correct adjustment sets
  - Outcome: exam
  - Exposure: podcast
  - Adjustments: alertness and prepared

  Identification of direct and total effects

  Model is correctly specified.
  All minimal sufficient adjustments to estimate the direct and total effect were done.

Code
  print(dag)
Output
  # Check for correct adjustment sets
  - Outcome: exam
  - Exposure: podcast
  - Adjustment: alertness

  Identification of direct and total effects

  Incorrectly adjusted!
  To estimate the direct and total effect, at least adjust for one of the following sets:
  - alertness, prepared
  - alertness, skills_course.
  Currently, the model only adjusts for `alertness`. You possibly also need to adjust for `prepared` and `skills_course` to block biasing paths.

check_dag, different adjustements for total and direct

Code
  print(dag)
Output
  # Check for correct adjustment sets
  - Outcome: outcome
  - Exposure: exposure

  Identification of direct effects

  Incorrectly adjusted!
  To estimate the direct effect, at least adjust for `x1` and `x2`. Currently, the model does not adjust for any variables.

  Identification of total effects

  Incorrectly adjusted!
  To estimate the total effect, at least adjust for `x1`. Currently, the model does not adjust for any variables.

Code
  print(dag)
Output
  # Check for correct adjustment sets
  - Outcome: outcome
  - Exposure: exposure
  - Adjustment: x1

  Identification of direct effects

  Incorrectly adjusted!
  To estimate the direct effect, at least adjust for `x1` and `x2`. Currently, the model only adjusts for `x1`. You possibly also need to adjust for `x2` to block biasing paths.

  Identification of total effects

  Model is correctly specified.
  All minimal sufficient adjustments to estimate the total effect were done.

Code
  print(dag)
Output
  # Check for correct adjustment sets
  - Outcome: outcome
  - Exposure: exposure
  - Adjustment: x2

  Identification of direct effects

  Incorrectly adjusted!
  To estimate the direct effect, at least adjust for `x1` and `x2`. Currently, the model only adjusts for `x2`. You possibly also need to adjust for `x1` to block biasing paths.

  Identification of total effects

  Incorrectly adjusted!
  To estimate the total effect, do not adjust for `x2`.

Code
  print(dag)
Output
  # Check for correct adjustment sets
  - Outcome: outcome
  - Exposure: exposure
  - Adjustments: x1 and x2

  Identification of direct effects

  Model is correctly specified.
  All minimal sufficient adjustments to estimate the direct effect were done.

  Identification of total effects

  Incorrectly adjusted!
  To estimate the total effect, do not adjust for some or all of `x1` and `x2`.

check_dag, collider bias

Code
  print(dag)
Output
  # Check for correct adjustment sets
  - Outcome: SMD_ICD11
  - Exposure: agegroup
  - Adjustments: edgroup3, gender_kid, pss4_kid_sum_2sd and residence

  Identification of direct effects

  Incorrectly adjusted!
  To estimate the direct effect, at least adjust for `edgroup3`, `gender_kid`, `pss4_kid_sum_2sd`, `residence` and `sm_h_total_kid`. Currently, the model only adjusts for `edgroup3`, `gender_kid`, `pss4_kid_sum_2sd` and `residence`. You possibly also need to adjust for `sm_h_total_kid` to block biasing paths.

  Identification of total effects

  Model is correctly specified.
  All minimal sufficient adjustments to estimate the total effect were done.

Code
  print(dag)
Output
  # Check for correct adjustment sets
  - Outcome: SMD_ICD11
  - Exposure: agegroup
  - Adjustments: edgroup3, gender_kid, pss4_kid_sum_2sd, residence and sm_h_total_kid
  - Collider: sm_h_total_kid

  Identification of direct effects

  Incorrectly adjusted!
  Your model adjusts for a potential collider. To estimate the direct effect, do not adjust for `sm_h_total_kid` to avoid collider-bias. It is recommended to double-check for the collider-bias on the dagitty-website.

  Identification of total effects

  Incorrectly adjusted!
  Your model adjusts for a potential collider. To estimate the total effect, do not adjust for `sm_h_total_kid` to avoid collider-bias. It is recommended to double-check for the collider-bias on the dagitty-website.