asre-compute 0.0.5

From a pandas dataframe, this program computes through A-learning the ARE or cognitive biais ASRE along their 95% confidence intervals.

From a pandas dataframe, this program computes the Absolute Rule Effect or cognitive biais Absolute Stochastic Rule Effect through A-learning and rpovides asymptotic 95% confidence intervals from M-estimation sandwitch formula.

asre_package takes as arguments

• df: pandas dataframe

• rule: column name for the rule as a string (random variable must be Bernoulli)

• ttt: column name for the experimental treatment as a string (random variable must be Bernoulli)

• y: column name for the outcome as a string (random variable can be either binary or continuous)

• ps_predictors: list of column names (strings) for variables causing experimental treatment initiation e.g.propensity score predictors (random variables can be either binary or continuous)

• pronostic_predictors: list of column names (strings) for variables causing the outcome e.g. prognosis predictors (random variables can be either binary or continuous)

• ctst_vrb: list of column names (strings) for variables acting as treatment effect modifiers e.g. contrast variables (random variables can be either binary or continuous)

• est=’ARE’: takes value ‘ARE’ computes only the Absolute Rule Effect or ‘ASRE_cb’ then the program computes ARE and cognitive biais ASRE with alpha value provided below

• alpha = .5: cognitive bias value for ASRE, if est=”ASRE_cb’

• n_alphas=5: number of alphas computed on the plot, if est=”ASRE_cb’

• precision=3: rounding of the printed ARE/ASRE and their 95% confidence intervals.

Change Log

0.0.5 (07/27/2021)

• Fix module structure

0.0.1 (07/27/2021)

• First Release